EP4699083A1 - A semantic model of eyelashes - Google Patents

Abstract

Some embodiments of a method include obtaining (702) a photo (302) of an eye; segmenting (704, 304) a first plurality of eyelashes from the photo (306); inpainting (706, 308) at least a subset of the plurality of eyelashes onto the photo to remove one or more of the first plurality of eyelashes from the photo (310); creating (708) a model of an area around the eye; creating (710) a second plurality of eyelashes using the model of the area around the eye; and rendering (712) the second plurality of eyelashes onto the photo (560).

Description

A SEMANTIC MODEL OF EYELASHES

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims benefit of European Patent Application No. EP23305802, entitled "A SEMANTIC MODEL OF EYELASHES” and filed May 19, 2023, which is hereby incorporated by reference in its entirety.

BACKGROUND

[0002] Eyelashes are largely responsible for the look and attractiveness of a human face. This observation helps explain the great interest in cosmetic design of the ocular region, particularly the eyelashes.

SUMMARY

[0003] Embodiments described herein include methods that are used in video encoding and decoding (collectively "coding”).

[0004] An example method in accordance with some embodiments may include: obtaining a photo of an eye; segmenting a first plurality of eyelashes from the photo; inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

[0005] For some embodiments of an example method, creating the model of the area around the eye comprises fitting a photometric eye model to the area around the eye.

[0006] An example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0007] Another example method in accordance with some embodiments may include: obtaining a photo of an eye; segmenting a plurality of eyelashes from the photo; and inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the plurality of eyelashes from the photo. [0008] Another example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0009] A further example method in accordance with some embodiments may include: obtaining a photo of an eye; performing a three-dimensional reconstruction of an area around the eye; and producing a fitted template of the area around the eye.

[0010] A further example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0011] Another further example method in accordance with some embodiments may include: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; and creating a second plurality of eyelashes using the model of the area around the eye.

[0012] Some embodiments of another further example method may further include rendering the second plurality of eyelashes onto the photo.

[0013] Another further example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0014] An additional example method in accordance with some embodiments may include: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

[0015] Some embodiments of an additional example method may further include determining one or more parameters associated with the second plurality of eyelashes.

[0016] Some embodiments of an additional example method may further include receiving a user parameter input corresponding to one of the one or more parameters; adjusting the second plurality of eyelashes based on the user parameter; and rendering the adjusted second plurality of eyelashes onto the photo.

[0017] An additional example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above. [0018] In additional embodiments, encoder and decoder apparatus are provided to perform the methods described herein. An encoder or decoder apparatus may include a processor configured to perform the methods described herein. The apparatus may include a computer-readable medium (e.g. a non-transitory medium) storing instructions for performing the methods described herein. In some embodiments, a computer-readable medium (e.g. a non-transitory medium) stores a video encoded using any of the methods described herein.

[0019] One or more of the present embodiments also provide a computer readable storage medium having stored thereon instructions for performing bi-directional optical flow, encoding or decoding video data according to any of the methods described above. The present embodiments also provide a computer readable storage medium having stored thereon a bitstream generated according to the methods described above. The present embodiments also provide a method and apparatus for transmitting the bitstream generated according to the methods described above. The present embodiments also provide a computer program product including instructions for performing any of the methods described.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020] FIG. 1A is a system diagram illustrating an example communications system according to some embodiments.

[0021] FIG. 1 B is a system diagram illustrating an example wireless transmit/receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to some embodiments.

[0022] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments.

[0023] FIG. 2A is a functional block diagram of block-based video encoder, such as an encoder used for Versatile Video Coding (VVC), according to some embodiments.

[0024] FIG. 2B is a functional block diagram of a block-based video decoder, such as a decoder used for WC, according to some embodiments.

[0025] FIG. 3A is a process diagram illustrating an example eyelash segmentation according to some embodiments.

[0026] FIG. 3B is a process diagram illustrating an example reconstruction and parametrization with and without eyelash removal according to some embodiments. [0027] FIG. 4A is a schematic illustration showing a first example visualization of an oriented point cloud obtained via a multi-view stereo process according to some embodiments.

[0028] FIG. 4B is a schematic illustration showing a second example visualization of an oriented point cloud obtained via a multi-view stereo process according to some embodiments.

[0029] FIG. 5A is a schematic illustration showing example raw 3D eyelashes after a 3D hair growing algorithm according to some embodiments.

[0030] FIG. 5B is a schematic illustration showing an example filtered 3D eyelashes after a 3D hair growing algorithm according to some embodiments.

[0031] FIG. 5C is a schematic illustration showing example raw 3D eyelashes projected on a view of an eye according to some embodiments.

[0032] FIG. 5D is a schematic illustration showing an example filtered 3D eyelashes projected on a view of an eye according to some embodiments.

[0033] FIG. 6 is a process diagram illustrating an example synthetic eyelash parameterized generation process according to some embodiments.

[0034] FIG. 7 is a flowchart illustrating an example process for creating a synthetic set of eyelashes according to some embodiments.

[0035] The entities, connections, arrangements, and the like that are depicted in— and described in connection with— the various figures are presented by way of example and not by way of limitation. As such, any and all statements or other indications as to what a particular figure "depicts,” what a particular element or entity in a particular figure "is” or "has,” and any and all similar statements— that may in isolation and out of context be read as absolute and therefore limiting— may only properly be read as being constructively preceded by a clause such as "In at least one embodiment, ... " For brevity and clarity of presentation, this implied leading clause is not repeated ad nauseum in the detailed description.

DETAILED DESCRIPTION

[0036] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0037] As shown in FIG. 1 A, the communications system 100 may include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104/113, a ON 106, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and/or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and/or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a "station” and/or a "STA”, may be configured to transmit and/or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a UE.

[0038] The communications systems 100 may also include a base station 114a and/or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106, the Internet 110, and/or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and/or network elements.

[0039] The base station 114a may be part of the RAN 104/113, which may also include other base stations and/or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and/or the base station 114b may be configured to transmit and/or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e., one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and/or receive signals in desired spatial directions.

[0040] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0041] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104/113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 116 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and/or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and/or High-Speed UL Packet Access (HSUPA).

[0042] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and/or LTE-Advanced (LTE-A) and/or LTE-Advanced Pro (LTE-A Pro).

[0043] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0044] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and/or transmissions sent to/from multiple types of base stations (e.g., a eNB and a gNB).

[0045] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0046] The base station 114b in FIG. 1A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the ON 106.

[0047] The RAN 104/113 may be in communication with the ON 106, which may be any type of network configured to provide voice, data, applications, and/or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The ON 106 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and/or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104/113 and/or the ON 106 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104/113 or a different RAT. For example, in addition to being connected to the RAN 104/113, which may be utilizing a NR radio technology, the ON 106 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology. [0048] The CN 106 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and/or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and/or the internet protocol (IP) in the TCP/IP internet protocol suite. The networks 112 may include wired and/or wireless communications networks owned and/or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104/113 or a different RAT.

[0049] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0050] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmi t/receive element 122, a speaker/microphone 124, a keypad 126, a display/touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and/or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0051] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit/receive element 122. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip. [0052] The transmit/receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit/receive element 122 may be an antenna configured to transmit and/or receive RF signals. In an embodiment, the transmit/receive element 122 may be an emitter/detector configured to transmit and/or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit/receive element 122 may be configured to transmit and/or receive both RF and light signals. It will be appreciated that the transmit/receive element 122 may be configured to transmit and/or receive any combination of wireless signals.

[0053] Although the transmit/receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit/receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0054] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit/receive element 122 and to demodulate the signals that are received by the transmit/receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11 , for example.

[0055] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0056] The processor 118 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium- ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0057] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and/or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable locationdetermination method while remaining consistent with an embodiment.

[0058] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and/or hardware modules that provide additional features, functionality and/or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and/or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and/or Augmented Reality (VR/AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and/or a humidity sensor.

[0059] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and/or simultaneous. The full duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)). [0060] Although the WTRU is described in FIGs. 1A-1 B as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0061] In representative embodiments, the other network 112 may be a WLAN.

[0062] In view of FIGs. 1A-1 B, and the corresponding description, one or more, or all, of the functions described herein may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and/or to simulate network and/or WTRU functions.

[0063] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and/or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and/or deployed as part of a wired and/or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented/deployed as part of a wired and/or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and/or may performing testing using over-the-air wireless communications.

[0064] The one or more emulation devices may perform the one or more, including all, functions while not being implemented/deployed as part of a wired and/or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and/or a non-deployed (e.g., testing) wired and/or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and/or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and/or receive data.

[0065] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments. An extended reality display device, together with its control electronics, may be implemented using a system such as the system of FIG. 1 D. System 150 can be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this document. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 150, singly or in combination, can be embodied in a single integrated circuit ( IC) , multiple ICs, and/or discrete components. For example, in at least one embodiment, the processing and encoder/decoder elements of system 150 are distributed across multiple ICs and/or discrete components. In various embodiments, the system 150 is communicatively coupled to one or more other systems, or other electronic devices, via, for example, a communications bus or through dedicated input and/or output ports. In various embodiments, the system 1000 is configured to implement one or more of the aspects described in this document.

[0066] The system 150 includes at least one processor 152 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document. Processor 152 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 150 includes at least one memory 154 (e.g., a volatile memory device, and/or a non-volatile memory device). System 150 may include a storage device 158, which can include non-volatile memory and/or volatile memory, including, but not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), flash, magnetic disk drive, and/or optical disk drive. The storage device 158 can include an internal storage device, an attached storage device (including detachable and non-detachable storage devices), and/or a network accessible storage device, as non-limiting examples.

[0067] System 150 includes an encoder/decoder module 156 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 156 can include its own processor and memory. The encoder/decoder module 156 represents module(s) that can be included in a device to perform the encoding and/or decoding functions. As is known, a device can include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 156 can be implemented as a separate element of system 150 or can be incorporated within processor 152 as a combination of hardware and software as known to those skilled in the art.

[0068] Program code to be loaded onto processor 152 or encoder/decoder 156 to perform the various aspects described in this document can be stored in storage device 158 and subsequently loaded onto memory 154 for execution by processor 152. In accordance with various embodiments, one or more of processor 152, memory 154, storage device 158, and encoder/decoder module 156 can store one or more of various items during the performance of the processes described in this document. Such stored items can include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

[0069] In some embodiments, memory inside of the processor 152 and/or the encoder/decoder module 156 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device can be either the processor 152 or the encoder/decoder module 152) is used for one or more of these functions. The external memory can be the memory 154 and/or the storage device 158, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of, for example, a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2 (MPEG refers to the Moving Picture Experts Group, MPEG-2 is also referred to as ISO/IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC refers to High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Versatile Video Coding, a new standard being developed by JVET, the Joint Video Experts Team).

[0070] The input to the elements of system 150 can be provided through various input devices as indicated in block 172. Such input devices include, but are not limited to, (i) a radio frequency (RF) portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Component (COMP) input terminal (or a set of COMP input terminals), (ill) a Universal Serial Bus (USB) input terminal, and/or (iv) a High Definition Multimedia Interface (HDMI) input terminal. Other examples, not shown in FIG. 1 C, include composite video.

[0071] In various embodiments, the input devices of block 172 have associated respective input processing elements as known in the art. For example, the RF portion can be associated with elements suitable for (I) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) downconverting the selected signal, (ill) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel in certain embodiments, (iv) demodulating the downconverted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion can include a tuner that performs various of these functions, including, for example, downconverting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, downconverting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions. Adding elements can include inserting elements in between existing elements, such as, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.

[0072] Additionally, the USB and/or HDMI terminals can include respective interface processors for connecting system 150 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or within processor 152 as necessary. Similarly, aspects of USB or HDMI interface processing can be implemented within separate interface ICs or within processor 152 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 152, and encoder/decoder 156 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.

[0073] Various elements of system 150 can be provided within an integrated housing, Within the integrated housing, the various elements can be interconnected and transmit data therebetween using suitable connection arrangement 174, for example, an internal bus as known in the art, including the Inter- IC (I2C) bus, wiring, and printed circuit boards.

[0074] The system 150 includes communication interface 160 that enables communication with other devices via communication channel 162. The communication interface 160 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 162. The communication interface 160 can include, but is not limited to, a modem or network card and the communication channel 162 can be implemented, for example, within a wired and/or a wireless medium.

[0075] Data is streamed, or otherwise provided, to the system 150, in various embodiments, using a wireless network such as a Wi-Fi network, for example IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers). The Wi-Fi signal of these embodiments is received over the communications channel 162 and the communications interface 160 which are adapted for Wi-Fi communications. The communications channel 162 of these embodiments is typically connected to an access point or router that provides access to external networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 150 using a set-top box that delivers the data over the HDMI connection of the input block 172. Still other embodiments provide streamed data to the system 150 using the RF connection of the input block 172. As indicated above, various embodiments provide data in a non-streaming manner. Additionally, various embodiments use wireless networks other than Wi-Fi, for example a cellular network or a Bluetooth network.

[0076] The system 150 can provide an output signal to various output devices, including a display 176, speakers 178, and other peripheral devices 180. The display 176 of various embodiments includes one or more of, for example, a touchscreen display, an organic light-emitting diode (OLED) display, a curved display, and/or a foldable display. The display 176 can be for a television, a tablet, a laptop, a cell phone (mobile phone), or other device. The display 176 can also be integrated with other components (for example, as in a smart phone), or separate (for example, an external monitor for a laptop). The other peripheral devices 180 include, in various examples of embodiments, one or more of a stand-alone digital video disc (or digital versatile disc) (DVR, for both terms), a disk player, a stereo system, and/or a lighting system. Various embodiments use one or more peripheral devices 180 that provide a function based on the output of the system 150. For example, a disk player performs the function of playing the output of the system 150.

[0077] In various embodiments, control signals are communicated between the system 150 and the display 176, speakers 178, or other peripheral devices 180 using signaling such as AV. Link, Consumer Electronics Control (CEC), or other communications protocols that enable device-to-device control with or without user intervention. The output devices can be communicatively coupled to system 1000 via dedicated connections through respective interfaces 164, 166, and 168. Alternatively, the output devices can be connected to system 150 using the communications channel 162 via the communications interface 160. The display 176 and speakers 178 can be integrated in a single unit with the other components of system 150 in an electronic device such as, for example, a television. In various embodiments, the display interface 164 includes a display driver, such as, for example, a timing controller (T Con) chip.

[0078] The display 176 and speaker 178 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 172 is part of a separate set-top box. In various embodiments in which the display 176 and speakers 178 are external components, the output signal can be provided via dedicated output connections, including, for example, HDM I ports, USB ports, or COMP outputs.

[0079] The system 150 may include one or more sensor devices 168. Examples of sensor devices that may be used include one or more GPS sensors, gyroscopic sensors, accelerometers, light sensors, cameras, depth cameras, microphones, and/or magnetometers. Such sensors may be used to determine information such as user's position and orientation. Where the system 150 is used as the control module for an extended reality display (such as control modules 124, 132), the user's position and orientation may be used in determining how to render image data such that the user perceives the correct portion of a virtual object or virtual scene from the correct point of view. In the case of head-mounted display devices, the position and orientation of the device itself may be used to determine the position and orientation of the user for the purpose of rendering virtual content. In the case of other display devices, such as a phone, a tablet, a computer monitor, or a television, other inputs may be used to determine the position and orientation of the user for the purpose of rendering content. For example, a user may select and/or adjust a desired viewpoint and/or viewing direction with the use of a touch screen, keypad or keyboard, trackball, joystick, or other input. Where the display device has sensors such as accelerometers and/or gyroscopes, the viewpoint and orientation used for the purpose of rendering content may be selected and/or adjusted based on motion of the display device.

[0080] The embodiments can be carried out by computer software implemented by the processor 152 or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The memory 154 can be of any type appropriate to the technical environment and can be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples. The processor 152 can be of any type appropriate to the technical environment, and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples.

Block-Based Video Coding

[0081] Like HEVC, the VVC is built upon the block-based hybrid video coding framework. FIG. 2A gives the block diagram of a block-based hybrid video encoding system 200. Variations of this encoder 200 are contemplated, but the encoder 200 is described below for purposes of clarity without describing all expected variations.

[0082] Before being encoded, a video sequence may go through pre-encoding processing (204), for example, applying a color transform to an input color picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of the input picture components in order to get a signal distribution more resilient to compression (for instance using a histogram equalization of one of the color components). Metadata can be associated with the pre-processing and attached to the bitstream. [0083] The input video signal 202 including a picture to be encoded is partitioned (206) and processed block by block in units of, for example, CUs. Different CUs may have different sizes. In VTM-1.0, a CU can be up to 128x128 pixels. However, different from the HEVC which partitions blocks only based on quadtrees, in the VTM-1 .0, a coding tree unit (CTU) is split into CUs to adapt to varying local characteristics based on quad/binary/ternary-tree. Additionally, the concept of multiple partition unit type in the HEVC is removed, such that the separation of CU, prediction unit (PU) and transform unit (TU) does not exist in the WC-1.0 anymore; instead, each CU is always used as the basic unit for both prediction and transform without further partitions. In the multi-type tree structure, a CTU is firstly partitioned by a quad-tree structure. Then, each quad-tree leaf node can be further partitioned by a binary and ternary tree structure. Different splitting types may be used, such as quaternary partitioning, vertical binary partitioning, horizontal binary partitioning, vertical ternary partitioning, and horizontal ternary partitioning.

[0084] In the encoder of FIG. 2A, spatial prediction (208) and/or temporal prediction (210) may be performed. Spatial prediction (or "intra prediction”) uses pixels from the samples of already coded neighboring blocks (which are called reference samples) in the same video picture/slice to predict the current video block. Spatial prediction reduces spatial redundancy inherent in the video signal. Temporal prediction (also referred to as "inter prediction” or "motion compensated prediction”) uses reconstructed pixels from the already coded video pictures to predict the current video block. Temporal prediction reduces temporal redundancy inherent in the video signal. A temporal prediction signal for a given CU may be signaled by one or more motion vectors (MVs) which indicate the amount and the direction of motion between the current CU and its temporal reference. Also, if multiple reference pictures are supported, a reference picture index may additionally be sent, which is used to identify from which reference picture in the reference picture store (212) the temporal prediction signal comes.

[0085] The mode decision block (214) in the encoder chooses the best prediction mode, for example based on a rate-distortion optimization method. This selection may be made after spatial and/or temporal prediction is performed. The intra/inter decision may be indicated by, for example, a prediction mode flag. The prediction block is subtracted from the current video block (216) to generate a prediction residual. The prediction residual is de-correlated using transform (218) and quantized (220). (For some blocks, the encoder may bypass both transform and quantization, in which case the residual may be coded directly without the application of the transform or quantization processes.) The quantized residual coefficients are inverse quantized (222) and inverse transformed (224) to form the reconstructed residual, which is then added back to the prediction block (226) to form the reconstructed signal of the CU. Further in-loop filtering, such as deblocking/SAO (Sample Adaptive Offset) filtering, may be applied (228) on the reconstructed CU to reduce encoding artifacts before it is put in the reference picture store (212) and used to code future video blocks. To form the output video bit-stream 230, coding mode (inter or intra), prediction mode information, motion information, and quantized residual coefficients are all sent to the entropy coding unit (108) to be further compressed and packed to form the bit-stream.

[0086] FIG. 2B gives a block diagram of a block-based video decoder 250. In the decoder 250, a bitstream is decoded by the decoder elements as described below. Video decoder 250 generally performs a decoding pass reciprocal to the encoding pass as described in FIG. 2A. The encoder 200 also generally performs video decoding as part of encoding video data.

[0087] In particular, the input of the decoder includes a video bitstream 252, which can be generated by video encoder 200. The video bit-stream 252 is first unpacked and entropy decoded at entropy decoding unit 254 to obtain transform coefficients, motion vectors, and other coded information. Picture partition information indicates how the picture is partitioned. The decoder may therefore divide (256) the picture according to the decoded picture partitioning information. The coding mode and prediction information are sent to either the spatial prediction unit 258 (if intra coded) or the temporal prediction unit 260 (if inter coded) to form the prediction block. The residual transform coefficients are sent to inverse quantization unit 262 and inverse transform unit 264 to reconstruct the residual block. The prediction block and the residual block are then added together at 266 to generate the reconstructed block. The reconstructed block may further go through in-loop filtering 268 before it is stored in reference picture store 270 for use in predicting future video blocks.

[0088] The decoded picture 272 may further go through post-decoding processing (274), for example, an inverse color transform (e.g. conversion from YCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverse of the remapping process performed in the pre-encoding processing (204). The post-decoding processing can use metadata derived in the pre-encoding processing and signaled in the bitstream. The decoded, processed video may be sent to a display device 276. The display device 276 may be a separate device from the decoder 250, or the decoder 250 and the display device 276 may be components of the same device.

[0089] Various methods and other aspects described in this disclosure can be used to modify modules of a video encoder 200 or decoder 250. Moreover, the systems and methods disclosed herein are not limited to WC or HEVC, and can be applied, for example, to other standards and recommendations, whether preexisting or future-developed, and extensions of any such standards and recommendations (including WC and HEVC). Unless indicated otherwise, or technically precluded, the aspects described in this disclosure can be used individually or in combination. [0090] Many 3D artists model eyelashes like any other hair, using guide-curves made of particles, which is a cumbersome process. Guide-curves are drawn by hair modeling artists and represent the main directions of an haircut. From them, final hair strands are extrapolated to populate the scalp. Even though eyelashes are hair, eyelashes display a specific distribution and shape of their own. Although hair segmentation, reconstruction, modeling, simulation, and rendering has been studied extensively, no semantic or art-driven method to model eyelashes is understood to exist.

[0091] Hair may be created with ready-to-use Visual Effects (VFX) grooming software. 3D artists may create hair using digital tools embedded in 3D software, such as Blender, Maya, Houdini, Zbrush, and Cinema4D. Such grooming software is understood to be based on the same approach: work with a scalp and guide-curves (or ribbons or meshes) to roughly define the hair shape. Artists start by positioning the anchor (or root) of the guide-curve on the scalp. This positioning may be done in multiple ways. Artists may paint a texture on a surface to define areas of hair and then set specific parameters for each area using a generation procedure. Surface areas may be manually selected. Brushes may be used to paint "live” hair on a surface. For high precision, individual hairs may be placed manually.

[0092] After adding guides to a mesh surface, typically the guides are shaped by manipulating their vertices all along the curve. To do so, several tools are available to comb hair such as: cut, twist, noise, clump, freeze, frizz, curl, and braid the hair. Such tools claim to improve the result with an automatic hair creation system.

[0093] Some of the differences between these software tools and the present application include some of the following processes: automatic hair creation, hair and eyelash modeling, single-view hair reconstruction, multi-view hair reconstruction, hair segmentation, hair rendering, hair simulation, single-view face reconstruction, and statistics-based modeling.

[0094] A data-driven model of eyelash statistics may be used to describe the global look of eyelashes. For some embodiments, the model may feature semantic knobs, e.g., control parameters to customize the look of eyelashes, such as density, radius, length, or clumping. For some embodiments, parameter distributions of this model are extracted from real reconstructed eyelashes. In accordance with some embodiments, a user-friendly tool may be used to edit eyelashes in real-time and validate performance of the model in an eyelash creation workflow. Using a model, synthetic images of eyelashes may be generated to train a neural network that estimates the statistical parameters from a photograph of an eye.

[0095] For example, a pipeline may be used for eyelash replacement from a photograph. For some embodiments, this pipeline may include eyelash segmentation and inpainting, photometric eye model fitting, eyelash generation with the model, and hair rendering. For some embodiments, synthetic data may be generated. Synthetic data also may be used to train a hair segmentation network. A neural network trained with such data may perform segmentation and, for some embodiments, the eyelash segmentation performance may be comparable to EyelashNet, a reference model in eyelash segmentation, but using only synthetic data.

[0096] Some embodiments may include a semantic data-driven and statistics-based eyelash model that represents a distribution of real eyelashes. Such a model may be used for eyelash creation and synthetic data generation. Furthermore, such a model may be used for real-time eyelash creation and editing. Some embodiments may include a process for eyelash replacement and editing using a single photograph. For some embodiments, a first neural network may be used to estimate parameters for a model in which the model was obtained from an image of an eye. For some embodiments, a second neural network may be used for eyelash segmentation. Such a second neural network may output results similar to EyelashNet while using only synthetic data for training.

Eyelash Cuts Dataset

[0097] For some embodiments, the eyelash model uses real eyelash data to produce plausible eyelash models. For some embodiments, eyelash data may be obtained by reconstructing the eyelashes using an eye region dataset described in the article, Kerbiriou, Glenn, et al., Detailed Eye Region Capture and Animation, 2022 COMPUTER GRAPHICS FORUM (2022), available at: doi.org/10.1111/cgf.14642 ‘Kerbiriou"). For some embodiments, an eyelash reconstruction pipeline may include eyelash segmentation, 2D eyelashes growing and filtering, multi-view segments matching, and 3D eyelashes growing and filtering. For some embodiments, an eyelash reconstruction pipeline may include: starting with a series (say 12, input images); performing 2D eyelashes computation to generate 2D eyelashes; performing segments matching to generate a 3D directional point cloud; performing 3D eyelashes growing to generate eyelash 3D paths; performing thickness and albedo optimization to generate reconstructed eyelashes.

Eyes Dataset

[0098] The example dataset of posed eyes built by Kerbiriou and containing more than 2,000 scans of eyes regions of 57 volunteers was used herein. For some embodiments, the eyelash processes described herein may operate on top a dataset, such as the one built by Kerbiriou. Each scan is reconstructed using photogrammetry from twelve views. Photogrammetry is a process of deriving 3D information from one or more photographs. According to the example, for each volunteer, 36 eye poses are reconstructed, allowing for geometrically accurate rigging by tri-linear interpolation. From this set, only neutral eye poses, which correspond to a forward gaze with eyes normally open, are used. In other words, for some embodiments, a neutral eye pose includes a forward gaze and a normal eyelid aperture. Such a neutral eye pose may represent only one of the 36 eye poses.

Eyelash Segmentation

[0099] FIG. 3A is a process diagram illustrating an example eyelash segmentation according to some embodiments. Segmenting the eyelashes not only allows for locating the eyelashes in the photo, but also for inpainting them out of the photo, thereby leading to better quality surface reconstructions. For some embodiments, an inpainting algorithm may remove the eyelashes and fill them in with nearby pixel data. For some embodiments, EyelashNet is used to segment images in the Kerbiriou dataset. Furthermore, for a new photo, for example, EyelashNet may be used to perform eyelash segmentation.

[0100] Journal article, Xiao, Qinjie, et al., EyelashNet: A Dataset and a Baseline Method for Eyelash Matting, 40:6 ACM TRANSACTIONS ON GRAPHICS 1-17 (Dec. 2021), available at: doi.org/10.1145/3478513.3480540, describes EyelashNet, a convolutional neural network (CNN) specifically trained to segment eyelashes. Segmentation is a process that highlights the border of an object. Regarding eyelashes, segmentation may include the creation of a binary mask (which may be a black and white image), which is used to filter the eyelashes (e.g., white) from the rest of the image (e.g., black). As part of an eyelash reconstruction, the EyelashNet CNN is used to perform a segmentation 304 of the eyelashes in each of twelve source image views 302. In Kerbiriou's dataset, there are 12 camera viewpoints. For some embodiments, there may be than 12 camera viewpoints or there may be less than 12 camera viewpoints. FIG. 3A shows just one of those twelve views of the eye. The example segmentation process 300 may perform an EyelashNet segmentation 304 to produce eyelash segmentations 306. Such EyelashNet segmentations 306 may be used to perform inpainting 308 and produce source images with inpainted eyelashes 310.

[0101] FIG. 3B is a process diagram illustrating an example reconstruction and parametrization with and without eyelash removal according to some embodiments. FIG. 3A presents the eyelash segmentation process, and FIG. 3B presents the 3D reconstruction with or without eyelash segmentation. The left set of 3 images of FIG. 3B show a process 330 without eyelash removal. The right set of 3 images of FIG. 3B show a process 360 with eyelash removal. The top two images 332, 362 of FIG. 3B are the original source images. The upper left image 332 retains the eyelashes, while the upper right image 362 has the eyelashes removed, which may be done by inpainting. For each of the original source images, a 3D reconstruction 334, 364 is performed to produce a scan mesh shown in the middle two images 336, 366. A template registration 338, 368 is performed for each scan mesh to produce a fitted template shown in the bottom two images 340, 370. Comparing the left 3 images with the right 3 images of FIG. 3B shows the impact of eyelash removal on the eye region reconstruction and parametrization process. For example, such impact may include undesired artifacts, such as holes or budges. Parameterization, for example, is a process performed to apply UV mapping (texture) to a reconstructed skin mesh.

[0102] Mesh registration is a technique of 3D reconstruction in which the goal is to deform the mesh to enable the mesh to fit another shape. So, the mesh topology (connectivity) is kept intact. There are other techniques in 3D reconstruction in which the goal is to build a new 3D mesh from scratch with no topology constraints. Template registration is a technique used to do 3D reconstruction of the eye region.

Statistics-Based Eyelashes Model

[0103] A statistics-based eyelash model is used that is data driven while enabling semantic control. For some embodiments, the model does not rely on precise eyelash shape but on distribution functions for the eyelashes' length, radius, and angles, among other items. The eyelashes model relies on the spatial distribution of the eyelash model parameters (see Table 1 below). All the individual eyelash related parameters are represented as functions of the horizontal root position (the u parameter) with profile curves. There is an additional eyelash density parameter, which informs about the distribution of the eyelash roots.

Table 1.

2D Segments Growing and Filtering

[0104] Journal article, Beeler, Thabo, et al., Coupled 3D Reconstruction of Sparse Facial Hair and Skin. 31 :4 ACM TRANS, ON GRAPHICS 1-10 (August 2012), available at: doi.org/10.1145/2185520.2185613 ("Bee/er"), describes growing 2D hairs in each image but the final result is in 3D. For the present application, 2D hairs are grown in each image, but the hair map obtained with image processing is replaced by Eyelash- Net's segmentation. This process leads to a more robust identification of eyelash pixels. The goal of this process is to obtain a great number of 2D segments in the direction of the eyelashes. A hair map may be obtained using oriented filters or Gabor filters. These filters are well suited to estimate the local orientation of hair on an image. As understood, EyelashNet does not use such filters because EyelashNet directly predicts the hair map from the original image using a deep neural network. Additionally, the 2D hairs are filtered using an orientation map predicted (using EyelashNet) from the segmentations. For each pixel belonging to a 2D eyelash, the angle difference between its support segment and the orientation map is computed. 2D eyelashes with an average angular distance greater than 40° relative to the orientation map are discarded. A support segment is a 3D segment that includes the current pixel. An orientation map is an image in which color indicates the orientation. The orientation color may go from red to blue. Multi-View Segment Matching and Filtering

[0105] FIG. 4A is a schematic illustration showing a first example visualization of an oriented point cloud obtained via a multi-view stereo process according to some embodiments. For some embodiments, the multiview stereo procedure of Beeler is used. Such a procedure takes multiple images as input and processes them to compute the 3D hairs. The resulting 3D segments shown in FIG. 4A are converted to an oriented point cloud by taking the midpoints of each segment and normalizing their end points difference. The oriented point cloud is filtered by a measure of confidence for each point.

[0106] An affinity score a between two oriented points = {p , d and Pj = {pj, dj} may be calculated as shown in Eq. 1 :

The affinity score is close to one when the two points share a similar direction and are well aligned. FIG. 4A shows an example mapping 400 of averaged affinity scores for a first example visualization of an oriented point cloud obtained via a multi-view stereo process. An oriented point may be seen as a vector with a point in the middle. Consider a person on a bike from a top view. Pt is the point (the object) which contains a position pt and a direction d_L.

[0107] FIG. 4B is a schematic illustration showing a second example visualization of an oriented point cloud obtained via a multi-view stereo process according to some embodiments. For each oriented point, a confidence score a is computed as the averaged affinity score of its k = 150 neighbors, as shown in Eq. 2: J- ₀ tq.

For some embodiments, all the points with a confidence score lower than 0.2 are removed from the oriented point cloud. Also, the confidence score a is shown in "false” colors that are not the color of the eyelashes. For example, the confidence is visualized on FIG. 4A, and FIG. 4B shows confidence from 0 (which may be blue in color versions showing confidence) to 1 (which may be red). The scale for confidence score is shown next to FIG. 4B. As a result of removing points with a confidence score lower than 0.2, synthetic eyelash points corresponding to "blue” and "purple” confidence scores of the color version of FIG. 4A are removed and not shown in the color version of FIG. 4B. FIG. 4B shows an example mapping 450 of averaged affinity scores for a second example visualization of an oriented point cloud obtained via a multi-view stereo process. [0108] FIG. 4A is a visualization of the directional point cloud obtained via multi-view stereo (confidence score in false colors). FIG. 4A shows all directed points. FIG. 4B shows only points with a confidence level greater than 0.2. For a particular oriented point, an affinity score is determined. An average affinity score of the oriented point's neighbors (say k=150) gives a confidence regarding the determined affinity score. Points with a confidence score less than 0.2 are then removed. The value k may be changed, but a value of k=150 seems to provide the best result for some embodiments. A smaller value of k produces over-localized confidence, while a greater value may compute the confidence level with points from other eyelashes.

3D Eyelashes Growing and Filtering

[0109] 3D eyelashes are grown using a method analogous to the ones mentioned in Beeler and journal article, Nam, Giljoo, et al., Strand-Accurate Multi-View Hair Capture, IN PROC, OF IEEE CONF. COMPUTER VISION AND PATTERN RECOGNITION 155-164 (2019), but only an oriented point cloud is used . The reconstructed eyelashes are in the form of a list of oriented points {P₀, Pi, - , PN}- The oriented point with the greatest averaged affinity score is selected as the start point. A search is begun for the next point in the two directions given by the orientation of the first selected point. Points outside a closed cone of length I = 0.7 millimeters with an aperture of 13.5 degrees oriented in one of the directions are discarded for some embodiments. These values are just examples, and other values may be used. The next point is computed by blending the candidate points C into a weighted average. For some embodiments, the weights are their affinity score relative to the current point:

[0110] The start point and the candidate points are removed from the oriented point cloud. This process is repeated for both ends while there are at least 3 points in the candidate points set. The new point with the highest averaged a is selected as a seed or start point. Rejection sampling is used, and any seed that is closer than 0.2 millimeters to any other eyelash is discarded. The 3D hair growing algorithm eventually stops when the oriented point cloud is empty. The resulting reconstruction is exhaustive but many eyelashes are invalid. Some eyelashes are too short; some eyelashes start from too far from the skin and some eyelashes coincide too much with others. The reconstructed eyelashes are filtered by removing eyelashes measuring less than 3 mm. Eyelashes in which the root is located more than 2 mm away from the skin surface are also discarded. If two eyelashes are closer on average of 0.1 mm or less, then the shortest one is removed. Such an average value may be determined by doing many tests for some embodiments.

[0111] FIG. 5A is a schematic illustration showing example raw 3D eyelashes after a 3D hair growing algorithm according to some embodiments. FIG. 5A shows a first image 500 without filtering.

[0112] FIG. 5B is a schematic illustration showing an example filtered 3D eyelashes after a 3D hair growing algorithm according to some embodiments. FIG. 5B shows a second image 520 with filtering. [0113] FIG. 5C is a schematic illustration showing example raw 3D eyelashes projected on a view of an eye according to some embodiments. FIG. 5C shows a third image 540 without filtering.

[0114] FIG. 5D is a schematic illustration showing an example filtered 3D eyelashes projected on a view of an eye according to some embodiments. FIG. 5D shows a fourth image 560 with filtering. Notice how filtering does not remove the longest and most important eyelashes. Colors are individually consistent in columns.

Model Description

[0115] The journal article, Glondu, Loeiz, et al., Example-Based Fractured Appearance, 31 :4 COMPUTER GRAPHICS FORUM 1547-1556 (2012), available at', doi.org/10.1111/j.1467-8659.2012.03151.x, is understood to describe example-based fracture patterns. In accordance with some embodiments, one goal for the model of the present application is not to reconstruct exactly an exemplar data, but to find a set of large scale parameters that determine a global look of the eyelashes. A statistical extraction scheme may allow many fracture patterns to be replicated with great perceptual similarity for some embodiments. Both fracture patterns and eyelash cuts are entropic structures. Such structures may have the same overall appearance even with two different, small-scale shapes. Fracture pattern looks may be determined by parameters such as fragment area, crack edge length, orientation or straightness. In the case of eyelash cuts, eyelash density, length, thickness, curvature, and angles at the root may be used for some embodiments.

Conclusion

[0116] A data-driven model of eyelash statistics is presented. The model describes the global look of eyelashes which features semantic knobs such as density, radius, length, and/or clumping. The parameter distributions for this model are extracted from real reconstructed eyelashes. In accordance with some example embodiments, a user-friendly tool may be used to edit eyelashes in real-time and have the performance of the model validated in an eyelash creation workflow. Synthetic images of eyelashes are generated using this model. The synthetic images of eyelashes may be used to train a neural network that estimates the statistical parameters from a single photo of an eye. This application also describes a pipeline for eyelash replacement from a single photograph.

[0117] For some embodiments, eyelash segmentation and inpainting, photometric eye model fitting, and eyelash generation may be used with the model followed by hair rendering. The synthetic data is also used to train a hair segmentation network. The eyelash segmentation uses only synthetic data and has performance comparable to Eyelash Net. [0118] FIG. 6 is a process diagram illustrating an example synthetic eyelash parameterized generation process according to some embodiments. Parameters 602 are selected as shown on the far left side of FIG. 6. These parameters 602 are inputs into an example statistics-based eyelash model 600. By adjusting at least one of these parameters 602, two sets of synthetic eyelashes with different parameters are created and added to the upper right picture 604 and lower right picture 606 of an eye.

[0119] For some embodiments, eyelashes transfer may be used to transfer eyelashes from a source to a target. For example, the eyelashes of an i-th row of a first matrix of eyelashes pictures are transferred to the j-th column of a second matrix of eyelashes pictures.

[0120] For some embodiments, semantic editing may be used to edit eyelashes. For example, two reference eyes are used to demonstrate the appearance, thereby preserving eyelashes resampling. The original parameters are resampled three times to generate new eyelashes arrangements which closely share the same overall look. The parameters may be swapped, and eyelashes may be sampled again three times to show that this property remains on other target morphologies.

[0121] FIG. 7 is a flowchart illustrating an example process for creating a synthetic set of eyelashes according to some embodiments. For some embodiments, an example process may include obtaining a photo of an eye. For some embodiments, the example process may further include segmenting a first plurality of eyelashes from the photo. For some embodiments, the example process may further include inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the first plurality of eyelashes from the photo. For some embodiments, the example process may further include creating a model of an area around the eye. For some embodiments, the example process may further include creating a second plurality of eyelashes using the model of the area around the eye. For some embodiments, the example process may further include rendering the second plurality of eyelashes onto the photo.

[0122] While the methods and systems in accordance with some embodiments are generally discussed in context of extended reality (XR), some embodiments may be applied to any XR contexts such as, e.g., virtual reality (VR) / mixed reality (MR) / augmented reality (AR) contexts. Also, although the term "head mounted display (HMD)” is used herein in accordance with some embodiments, some embodiments may be applied to a wearable device (which may or may not be attached to the head) capable of, e.g., XR, VR, AR, and/or MR for some embodiments.

[0123] An example method in accordance with some embodiments may include: obtaining a photo of an eye; segmenting a first plurality of eyelashes from the photo; inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

[0124] For some embodiments of an example method, creating the model of the area around the eye comprises fitting a photometric eye model to the area around the eye.

[0125] An example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0126] Another example method in accordance with some embodiments may include: obtaining a photo of an eye; segmenting a plurality of eyelashes from the photo; and inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the plurality of eyelashes from the photo.

[0127] Another example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0128] A further example method in accordance with some embodiments may include: obtaining a photo of an eye; performing a three-dimensional reconstruction of an area around the eye; and producing a fitted template of the area around the eye.

[0129] A further example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0130] Another further example method in accordance with some embodiments may include: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; and creating a second plurality of eyelashes using the model of the area around the eye.

[0131] Some embodiments of another further example method may further include rendering the second plurality of eyelashes onto the photo.

[0132] Another further example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0133] An additional example method in accordance with some embodiments may include: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

[0134] Some embodiments of an additional example method may further include determining one or more parameters associated with the second plurality of eyelashes.

[0135] Some embodiments of an additional example method may further include receiving a user parameter input corresponding to one of the one or more parameters; adjusting the second plurality of eyelashes based on the user parameter; and rendering the adjusted second plurality of eyelashes onto the photo.

[0136] An additional example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform a method listed above.

[0137] This disclosure describes a variety of aspects, including tools, features, embodiments, models, approaches, etc. Many of these aspects are described with specificity and, at least to show the individual characteristics, are often described in a manner that may sound limiting. However, this is for purposes of clarity in description, and does not limit the disclosure or scope of those aspects. Indeed, all of the different aspects can be combined and interchanged to provide further aspects. Moreover, the aspects can be combined and interchanged with aspects described in earlier filings as well.

[0138] The aspects described and contemplated in this disclosure can be implemented in many different forms. While some embodiments are illustrated specifically, other embodiments are contemplated, and the discussion of particular embodiments does not limit the breadth of the implementations. At least one of the aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded. These and other aspects can be implemented as a method, an apparatus, a computer readable storage medium having stored thereon instructions for encoding or decoding video data according to any of the methods described, and/or a computer readable storage medium having stored thereon a bitstream generated according to any of the methods described.

[0139] In the present disclosure, the terms "reconstructed” and "decoded” may be used interchangeably, the terms "pixel” and "sample” may be used interchangeably, the terms "image,” "picture” and "frame” may be used interchangeably. Usually, but not necessarily, the term "reconstructed” is used at the encoder side while "decoded” is used at the decoder side. [0140] The terms HDR (high dynamic range) and SDR (standard dynamic range) often convey specific values of dynamic range to those of ordinary skill in the art. However, additional embodiments are also intended in which a reference to HDR is understood to mean "higher dynamic range” and a reference to SDR is understood to mean "lower dynamic range.” Such additional embodiments are not constrained by any specific values of dynamic range that might often be associated with the terms "high dynamic range” and "standard dynamic range.”

[0141] Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined. Additionally, terms such as "first”, "second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., such as, for example, a "first decoding” and a "second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.

[0142] Various numeric values may be used in the present disclosure, for example. The specific values are for example purposes and the aspects described are not limited to these specific values.

[0143] Embodiments described herein may be carried out by computer software implemented by a processor or other hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The processor can be of any type appropriate to the technical environment and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as nonlimiting examples.

[0144] Various implementations involve decoding. "Decoding”, as used in this disclosure, can encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display. In various embodiments, such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding. In various embodiments, such processes also, or alternatively, include processes performed by a decoder of various implementations described in this disclosure, for example, extracting a picture from a tiled (packed) picture, determining an upsampling filter to use and then upsampling a picture, and flipping a picture back to its intended orientation. [0145] As further examples, in one embodiment "decoding” refers only to entropy decoding, in another embodiment "decoding” refers only to differential decoding, and in another embodiment "decoding” refers to a combination of entropy decoding and differential decoding. Whether the phrase "decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions.

[0146] Various implementations involve encoding. In an analogous way to the above discussion about "decoding”, "encoding” as used in this disclosure can encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream. In various embodiments, such processes include one or more of the processes typically performed by an encoder, for example, partitioning, differential encoding, transformation, quantization, and entropy encoding. In various embodiments, such processes also, or alternatively, include processes performed by an encoder of various implementations described in this disclosure.

[0147] As further examples, in one embodiment "encoding” refers only to entropy encoding, in another embodiment "encoding” refers only to differential encoding, and in another embodiment "encoding” refers to a combination of differential encoding and entropy encoding. Whether the phrase "encoding process” is intended to refer specifically to a subset of operations or generally to the broader encoding process will be clear based on the context of the specific descriptions.

[0148] When a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method/process.

[0149] Various embodiments refer to rate distortion optimization. In particular, during the encoding process, the balance or trade-off between the rate and distortion is usually considered, often given the constraints of computational complexity. The rate distortion optimization is usually formulated as minimizing a rate distortion function, which is a weighted sum of the rate and of the distortion. There are different approaches to solve the rate distortion optimization problem. For example, the approaches may be based on an extensive testing of all encoding options, including all considered modes or coding parameters values, with a complete evaluation of their coding cost and related distortion of the reconstructed signal after coding and decoding. Faster approaches may also be used, to save encoding complexity, in particular with computation of an approximated distortion based on the prediction or the prediction residual signal, not the reconstructed one. A mix of these two approaches can also be used, such as by using an approximated distortion for only some of the possible encoding options, and a complete distortion for other encoding options. Other approaches only evaluate a subset of the possible encoding options. More generally, many approaches employ any of a variety of techniques to perform the optimization, but the optimization is not necessarily a complete evaluation of both the coding cost and related distortion.

[0150] The implementations and aspects described herein can be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed can also be implemented in other forms (for example, an apparatus or program). An apparatus can be implemented in, for example, appropriate hardware, software, and firmware. The methods can be implemented in, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants ("PDAs”), and other devices that facilitate communication of information between end-users.

[0151] Reference to "one embodiment” or "an embodiment” or "one implementation” or "an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase "in one embodiment” or "in an embodiment” or "in one implementation” or "in an implementation”, as well any other variations, appearing in various places throughout this disclosure are not necessarily all referring to the same embodiment.

[0152] Additionally, this disclosure may refer to "determining” various pieces of information. Determining the information can include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

[0153] Further, this disclosure may refer to "accessing” various pieces of information. Accessing the information can include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0154] Additionally, this disclosure may refer to "receiving” various pieces of information. Receiving is, as with "accessing”, intended to be a broad term. Receiving the information can include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, "receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0155] It is to be appreciated that the use of any of the following 7”, "and/or”, and "at least one of, for example, in the cases of “A/B”, "A and/or B” and "at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of "A, B, and/or C” and "at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended for as many items as are listed.

[0156] Also, as used herein, the word "signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a particular one of a plurality of parameters for region-based filter parameter selection for de-artifact filtering. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word "signal”, the word "signal” can also be used herein as a noun.

[0157] Implementations can produce a variety of signals formatted to carry information that can be, for example, stored or transmitted. The information can include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal can be formatted to carry the bitstream of a described embodiment. Such a signal can be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries can be, for example, analog or digital information. The signal can be transmitted over a variety of different wired or wireless links, as is known. The signal can be stored on a processor-readable medium. [0158] Note that various hardware elements of one or more of the described embodiments are referred to as "modules” that carry out (i.e., perform, execute, and the like) various functions that are described herein in connection with the respective modules. As used herein, a module includes hardware (e.g., one or more processors, one or more microprocessors, one or more microcontrollers, one or more microchips, one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more memory devices) deemed suitable by those of skill in the relevant art for a given implementation. Each described module may also include instructions executable for carrying out the one or more functions described as being carried out by the respective module, and it is noted that those instructions could take the form of or include hardware (i.e., hardwired) instructions, firmware instructions, software instructions, and/or the like, and may be stored in any suitable non-transitory computer-readable medium or media, such as commonly referred to as RAM, ROM, etc.

[0159] Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A method comprising: obtaining a photo of an eye; segmenting a first plurality of eyelashes from the photo; inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

2. The method of claim 1 , wherein creating the model of the area around the eye comprises fitting a photometric eye model to the area around the eye.

3. The method of any one of claims 1-2, further comprising determining one or more parameters associated with the second plurality of eyelashes.

4. The method of claim 3, further comprising: receiving a user parameter input corresponding to one of the one or more parameters; adjusting the second plurality of eyelashes based on the user parameter; and rendering the adjusted second plurality of eyelashes onto the photo.

5. The method of any one of claims 1-4, further comprising performing a three-dimensional reconstruction of an area around the eye; and producing a fitted template of the area around the eye.

6. The method of any one of claims 1-5, further comprising removing the first plurality of eyelashes from the photo.

7. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 1 through 6.

8. A method comprising: obtaining a photo of an eye; segmenting a plurality of eyelashes from the photo; and inpainting at least a subset of the plurality of eyelashes onto the photo to remove one or more of the plurality of eyelashes from the photo.

9. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of claim 8.

10. A method comprising: obtaining a photo of an eye; performing a three-dimensional reconstruction of an area around the eye; and producing a fitted template of the area around the eye.

11 . An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of claim 10.

12. A method comprising: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; and creating a second plurality of eyelashes using the model of the area around the eye.

13. The method of claim 12, further comprising: rendering the second plurality of eyelashes onto the photo.

14. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 12 through 13.

15. A method comprising: obtaining a photo of an eye; removing a first plurality of eyelashes from the photo; creating a model of an area around the eye; creating a second plurality of eyelashes using the model of the area around the eye; and rendering the second plurality of eyelashes onto the photo.

16. The method of claim 15, further comprising determining one or more parameters associated with the second plurality of eyelashes.

17. The method of claim 16, further comprising: receiving a user parameter input corresponding to one of the one or more parameters; adjusting the second plurality of eyelashes based on the user parameter; and rendering the adjusted second plurality of eyelashes onto the photo.

18. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 15 through 17.