WO2024085366A1 - Procédé de mise en oeuvre de mouvement de caméra au moyen d'une caméra virtuelle - Google Patents

Procédé de mise en oeuvre de mouvement de caméra au moyen d'une caméra virtuelle Download PDF

Info

Publication number
WO2024085366A1
WO2024085366A1 PCT/KR2023/010387 KR2023010387W WO2024085366A1 WO 2024085366 A1 WO2024085366 A1 WO 2024085366A1 KR 2023010387 W KR2023010387 W KR 2023010387W WO 2024085366 A1 WO2024085366 A1 WO 2024085366A1
Authority
WO
WIPO (PCT)
Prior art keywords
camera
focus
distance
virtual
real
Prior art date
Application number
PCT/KR2023/010387
Other languages
English (en)
Korean (ko)
Inventor
박수용
Original Assignee
주식회사 비브스튜디오스
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 주식회사 비브스튜디오스 filed Critical 주식회사 비브스튜디오스
Publication of WO2024085366A1 publication Critical patent/WO2024085366A1/fr

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/204—Image signal generators using stereoscopic image cameras
    • H04N13/246—Calibration of cameras
    • G—PHYSICS
    • G02—OPTICS
    • G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
    • G02B13/00—Optical objectives specially designed for the purposes specified below
    • G02B13/22—Telecentric objectives or lens systems
    • G—PHYSICS
    • G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
    • G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
    • G03B13/00—Viewfinders; Focusing aids for cameras; Means for focusing for cameras; Autofocus systems for cameras
    • G03B13/18—Focusing aids
    • G03B13/20—Rangefinders coupled with focusing arrangements, e.g. adjustment of rangefinder automatically focusing camera
    • G03B13/22—Rangefinders coupled with focusing arrangements, e.g. adjustment of rangefinder automatically focusing camera coupling providing for compensation upon change of camera lens
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/111—Transformation of image signals corresponding to virtual viewpoints, e.g. spatial image interpolation
    • H04N13/117—Transformation of image signals corresponding to virtual viewpoints, e.g. spatial image interpolation the virtual viewpoint locations being selected by the viewers or determined by viewer tracking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/10—Processing, recording or transmission of stereoscopic or multi-view image signals
    • H04N13/106—Processing image signals
    • H04N13/156—Mixing image signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/204—Image signal generators using stereoscopic image cameras
    • H04N13/243—Image signal generators using stereoscopic image cameras using three or more 2D image sensors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N13/00—Stereoscopic video systems; Multi-view video systems; Details thereof
    • H04N13/20—Image signal generators
    • H04N13/296—Synchronisation thereof; Control thereof
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/67—Focus control based on electronic image sensor signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/04—Synchronising
    • H04N5/06—Generation of synchronising signals

Definitions

  • This disclosure relates to a method of implementing camera movement, and more specifically, to a method of implementing realistic camera movement using a virtual camera.
  • the camera could not focus on the target in the virtual environment displayed by LED, and could only focus on the actual target.
  • the present disclosure has been derived based at least on the technical background examined above, but the technical problem or purpose of the present disclosure is not limited to solving the problems or shortcomings examined above.
  • the present disclosure can cover various technical issues related to the content to be described below.
  • the present disclosure aims to solve the problem of implementing realistic camera movements using a virtual camera.
  • a method performed by a computing device for realizing the above-described problem is disclosed.
  • the method includes synchronizing a virtual camera and a real camera; measuring a first distance from the synchronized camera position to the screen; selecting an object to focus on and measuring the focal distance for the selected object at the synchronized camera position; and adjusting the focus of at least one of the real camera and the virtual camera based on the first distance and the focal distance.
  • synchronizing the virtual camera and the real camera may include synchronizing at least one of position information, lens information, lens distortion information, or motion information of the real camera with the virtual camera.
  • the screen may include a screen that is output based on the synchronized camera and displays objects in a virtual environment.
  • the object to be focused on may include at least one of an object in the real environment or an object in the output virtual environment.
  • adjusting the focus of at least one of the real camera or the virtual camera based on the first distance and the focal length may include adjusting the focus of the real camera when the focal distance is less than or equal to the first distance. It may include an adjustment step.
  • adjusting the focus of the real camera may include: adjusting the focus of the virtual camera to the foreground when the focal length is less than or equal to the first distance, and adjusting the focus of the virtual camera to the foreground and It may include adjusting the focus of the camera.
  • adjusting the focus of at least one of the real camera or the virtual camera based on the first distance and the focal length may include adjusting the focus of the virtual camera when the focal distance is greater than the first distance. It may include a step of adjusting.
  • adjusting the focus of the virtual camera may include locking the focus of the real camera to the screen when the focal distance is above the first distance, and It may include adjusting the focus of the virtual camera.
  • a computer program stored in a computer-readable storage medium When executed on one or more processors, the computer program causes the one or more processors to perform operations for post-processing an image, the operations including: synchronizing a virtual camera and a real camera; measuring a first distance from the synchronized camera position to a screen; selecting an object to focus on and measuring a focal distance for the selected object at the synchronized camera position; and adjusting the focus of at least one of the real camera and the virtual camera based on the first distance and the focal distance.
  • a computing device for realizing the above-described problem is disclosed.
  • the device includes at least one processor; and a memory, wherein the processor synchronizes a virtual camera and a real camera; measure a first distance from the synchronized camera position to the screen; select an object to focus on and measure the focal distance for the selected object at the synchronized camera position; And may be configured to adjust the focus of at least one of the real camera and the virtual camera based on the first distance and the focal distance.
  • This disclosure can achieve the effect of achieving more realistic results and free production by breaking away from the limitation of not being able to focus on a virtual object output by LED in a virtual production environment using LED.
  • FIG. 1 is a block diagram of a computing device for implementing camera movement using a virtual camera according to an embodiment of the present disclosure.
  • Figure 2 is a schematic diagram showing a network function according to an embodiment of the present disclosure.
  • Figure 3 is a flowchart showing a method for implementing camera movement using a virtual camera according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic diagram illustrating a process of synchronizing a virtual camera and a real camera, measuring a first distance from the screen at the synchronized camera position, and an object to be focused on with the screen according to an embodiment of the present disclosure.
  • Figure 5 is a schematic diagram illustrating a process of selecting an object to focus on and measuring the focal distance for the selected object at a synchronized camera position according to an embodiment of the present disclosure.
  • FIG. 6A is a schematic diagram illustrating a process for adjusting the focus of an actual camera when the focal distance is less than or equal to a first distance according to an embodiment of the present disclosure.
  • FIG. 6B is a schematic diagram illustrating a process for adjusting the focus of a virtual camera when the focal distance exceeds a first distance according to an embodiment of the present disclosure.
  • FIG. 7 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.
  • a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer.
  • an application running on a computing device and the computing device can be a component.
  • One or more components may reside within a processor and/or thread of execution.
  • a component may be localized within one computer.
  • a component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon.
  • Components may transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.
  • data packets e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet.
  • a network such as the Internet
  • the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.
  • the term “at least one of A or B” should be interpreted to mean “a case containing only A,” “a case containing only B,” and “a case of combining A and B.”
  • network function artificial neural network, and neural network may be used interchangeably.
  • FIG. 1 is a block diagram of a computing device for implementing camera movement using a virtual camera according to an embodiment of the present disclosure.
  • the configuration of the computing device 100 shown in FIG. 1 is only a simplified example.
  • the computing device 100 may include different components for performing the computing environment of the computing device 100, and only some of the disclosed components may configure the computing device 100.
  • the computing device 100 may include a processor 110, a memory 130, and a network unit 150.
  • the processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit) may include a processor for data analysis and deep learning.
  • the processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present disclosure. According to an embodiment of the present disclosure, the processor 110 may perform an operation for learning a neural network model.
  • the processor 110 processes input data for learning in deep learning (DL), extracts features from input data, calculates errors, and learns neural network models such as updating the weights of the neural network model using backpropagation. You can perform calculations for .
  • DL deep learning
  • At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the neural network model.
  • the CPU and GPGPU can work together to process neural network model learning and data classification using the neural network model.
  • the processors of a plurality of computing devices can be used together to process learning of a neural network model and data classification using the neural network model.
  • a computer program executed in a computing device may be a CPU, GPGPU, or TPU executable program.
  • the memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150.
  • the memory 130 is a flash memory type, hard disk type, multimedia card micro type, or card type memory (e.g. (e.g. SD or -Only Memory), and may include at least one type of storage medium among magnetic memory, magnetic disk, and optical disk.
  • the computing device 100 may operate in connection with web storage that performs a storage function of the memory 130 on the Internet.
  • the description of the memory described above is merely an example, and the present disclosure is not limited thereto.
  • the network unit 150 includes Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL (A variety of wired communication systems can be used, such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).
  • PSTN Public Switched Telephone Network
  • xDSL Digital Subscriber Line
  • RADSL Rate Adaptive DSL
  • MDSL Multi Rate DSL
  • VDSL VDSL
  • wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN).
  • the network unit 150 presented in the present disclosure includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA (A variety of wireless communication systems can be used, such as Single Carrier-FDMA) and other systems.
  • CDMA Code Division Multi Access
  • TDMA Time Division Multi Access
  • FDMA Frequency Division Multi Access
  • OFDMA Orthogonal Frequency Division Multi Access
  • SC-FDMA A variety of wireless communication systems can be used, such as Single Carrier-FDMA and other systems.
  • the network unit 150 may be configured regardless of the communication mode, such as wired or wireless, and may be composed of various communication networks such as a personal area network (PAN) and a wide area network (WAN). You can. Additionally, the network may be the well-known World Wide Web (WWW), and may also use wireless transmission technology used for short-distance communication, such as Infrared Data Association (IrDA) or Bluetooth. The techniques described in this disclosure can also be used in other networks mentioned above.
  • WWW World Wide Web
  • IrDA Infrared Data Association
  • Bluetooth wireless transmission technology used for short-distance communication
  • Figure 2 is a schematic diagram showing a network function according to an embodiment of the present disclosure.
  • a neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons.
  • a neural network consists of at least one node. Nodes (or neurons) that make up neural networks may be interconnected by one or more links.
  • one or more nodes connected through a link may form a relative input node and output node relationship.
  • the concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa.
  • input node to output node relationships can be created around links.
  • One or more output nodes can be connected to one input node through a link, and vice versa.
  • the value of the data of the output node may be determined based on the data input to the input node.
  • the link connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.
  • one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network.
  • the characteristics of the neural network may be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if the same number of nodes and links exist and two neural networks with different weight values of the links exist, the two neural networks may be recognized as different from each other.
  • a neural network may consist of a set of one or more nodes.
  • a subset of nodes that make up a neural network can form a layer.
  • Some of the nodes constituting the neural network may form one layer based on the distances from the first input node.
  • a set of nodes with a distance n from the initial input node may constitute n layers.
  • the distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node.
  • this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above.
  • a layer of nodes may be defined by distance from the final output node.
  • the initial input node may refer to one or more nodes in the neural network through which data is directly input without going through links in relationships with other nodes. Alternatively, in the relationship between nodes based on links within a neural network, it may refer to nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the neural network. Additionally, hidden nodes may refer to nodes constituting a neural network other than the first input node and the last output node.
  • the neural network according to an embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. You can.
  • the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is.
  • the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. You can.
  • a neural network according to another embodiment of the present disclosure may be a neural network that is a combination of the above-described neural networks.
  • a deep neural network may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), etc.
  • CNN convolutional neural networks
  • RNN recurrent neural networks
  • GAN generative adversarial networks
  • RBM restricted Boltzmann machines
  • DBN deep belief network
  • Q network Q network
  • U network Siamese network
  • the network function may include an autoencoder.
  • An autoencoder may be a type of artificial neural network to output output data similar to input data.
  • the autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between input and output layers.
  • the number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically and reduced from the bottleneck layer to the output layer (symmetrical to the input layer).
  • Autoencoders can perform nonlinear dimensionality reduction.
  • the number of input layers and output layers can be corresponded to the dimension after preprocessing of the input data.
  • the number of nodes in the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, not enough information may be conveyed, so if it is higher than a certain number (e.g., more than half of the input layers, etc.) ) may be maintained.
  • a certain number e.g., more than half of the input layers, etc.
  • a neural network may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
  • Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.
  • Neural networks can be trained to minimize output errors.
  • neural network learning learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation.
  • teacher learning learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher learning, the correct answer may not be labeled in each learning data.
  • the learning data may be data in which each learning data is labeled with a category.
  • Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data.
  • the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate.
  • the neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch).
  • the learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stages of training, a low learning rate can be used to increase accuracy.
  • the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle.
  • Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, dropout to disable some of the network nodes during the learning process, and use of a batch normalization layer can be applied. You can.
  • Figure 3 is a flowchart showing a method for implementing camera movement using a virtual camera according to an embodiment of the present disclosure.
  • the computing device 100 may directly obtain information about an actual camera or receive it from an external system.
  • the external system may be a server, database, etc. that stores and manages data to implement camera movement.
  • the computing device 100 may use information about a real camera directly acquired or received from an external system as “input data for implementing camera movement using a virtual camera.”
  • the computing device 100 may synchronize the virtual camera and the real camera (S110). Specifically, the computing device 100 may synchronize at least one of location information, lens information, lens distortion information, or motion information of the real camera with the virtual camera. For example, during the shooting process, the degree to which a captured object is expressed may vary depending on the lens information of the actual camera, and the depth of output may be determined according to lens distortion information.
  • the computing device 100 synchronizes the lens information of the virtual camera 10-2 with the lens information of the real camera 10-1, and synchronizes the lens distortion information of the virtual camera 10-2 with the real camera (10-1).
  • the degree to which the photographed object is expressed in the image viewed from the viewpoint of the virtual camera and the depth of the output can be the same as in the case of the real camera.
  • the specific process of synchronizing the virtual camera and the real camera will be described later with reference to FIG. 4.
  • the computing device 100 may measure the first distance from the screen at the camera position synchronized through step S110 (S120). Specifically, the measured first distance means the physical distance from the screen at the synchronized camera position, and the first distance may be used in the process of adjusting the focus of at least one of the real camera or the virtual camera. Specific description will be described later through FIGS. 6A to 6B. Meanwhile, the screen is output based on the synchronized camera, and a virtual environment or an object of the virtual environment can be output. Additionally, the real-time movement of the synchronized camera may be simulated and displayed on the screen.
  • real-time movement of the synchronized camera can be obtained through camera position information and movement information acquired through a real-time camera tracking module, and the screen displays an image of the virtual environment and the synchronized camera.
  • An image simulating real-time movement can be synthesized and output. For example, if camera position information and movement information are obtained from the perspective of looking up at the sky from the floor with the synchronized camera, an image captured by the synchronized camera from the perspective of looking up at the sky from the floor in a virtual environment is displayed on the screen. can be printed.
  • the 3D renderer may refer to a device or program that performs a system graphics processing process to display a realistic shape by adding three-dimensional effect to the color, texture, or shadow of the image.
  • the computing device 100 may select an object to focus on and measure the focal distance for the selected object at the synchronized camera position through step S110 (S130).
  • the object to be focused on may include at least one of an object in the real environment or an object in the output virtual environment, and the object in the virtual environment output on the screen may include depth information.
  • the specific process in which an object to be focused on is selected and the focal distance for the selected object at the synchronized camera position is measured is described later in FIG. 5.
  • the computing device 100 may adjust the focus of at least one of the real camera or the virtual camera of the synchronized camera through step S110 based on the first distance measured through step S120 and the focal distance measured through step S130 ( S140). Specifically, when the focal distance is less than or equal to the first distance, the computing device 100 may adjust the focus of the virtual camera to the foreground and adjust the focus of the real camera. Additionally, according to another embodiment of the present disclosure, the computing device 100 may fix the focus of the real camera on the screen and adjust the focus of the virtual camera when the focal distance exceeds the first distance.
  • the reason for adjusting the focus of the virtual camera and the focus of the real camera in the synchronized camera is that if the focus of the virtual camera and the focus of the real camera are made the same, when the depth of field of the real camera is shallow, the image of the virtual camera Since the depth of field is shallow, when shooting with an actual camera, the output with a shallow depth of field is made shallower again, which may cause the problem of incorrect depth of field being applied to the output screen.
  • the focus when adjusting the focus of at least one of the real camera or the virtual camera, the focus may be adjusted using a focus controller. For example, based on the calculated focus value, the focus controller controls the focus of the actual camera by sending an appropriate value to a device with an actual physical motor (e.g., a motor device such as follow focus) ), or 2 you can control the focus of the virtual camera by sending a value appropriate for the virtual camera to the 3D renderer. Accordingly, the focus controller can transmit focus values to the real camera and the virtual camera simultaneously. The two focus values may be the same or different depending on the object to be focused on. Meanwhile, follow focus can generally be controlled by turning a gear or wired or wirelessly.
  • a focus controller controls the focus of the actual camera by sending an appropriate value to a device with an actual physical motor (e.g., a motor device such as follow focus) ), or 2 you can control the focus of the virtual camera by sending a value appropriate for the virtual camera to the 3D renderer.
  • the focus controller can transmit focus values to the real
  • the focus controller and follow focus are only disclosed as examples of adjusting the focus of a camera, but the present disclosure is not limited to this and various examples may be used to adjust the focus of the real camera or the virtual camera.
  • a specific process of adjusting the focus of at least one of the real camera or the virtual camera based on the first distance and the focal distance will be described later with reference to FIGS. 6A and 6B.
  • FIG. 4 is a schematic diagram illustrating a process of synchronizing a virtual camera and a real camera, measuring a first distance from the screen at the synchronized camera position, and an object to be focused on with the screen according to an embodiment of the present disclosure.
  • the computing device 100 uses information about the real camera 10-1 directly acquired or received from an external system to "track the movement of the camera using a virtual camera.” “Input data for implementation” can be used.
  • information about the actual camera 10-1 may include camera model, type of lens, thickness of lens, focal length, lens distortion information, location information, or movement information.
  • various information may be included. may be included.
  • the computing device 100 may synchronize the real camera 10-1 and the virtual camera 10-2 and obtain the synchronized camera 10. Specifically, the computing device 100 may synchronize at least one of location information, lens information, lens distortion information, or motion information of the real camera with the virtual camera. For example, during the shooting process, the degree to which a captured object is expressed may vary depending on the lens information of the actual camera 10-1, and the depth of output may be determined according to lens distortion information. The computing device 100 synchronizes the lens information of the virtual camera 10-2 with the lens information of the real camera 10-1, and synchronizes the lens distortion information of the virtual camera 10-2 with the real camera (10-1).
  • the computing device 100 may measure a first distance 12 from the screen 11 at the synchronized camera 10 position.
  • the measured first distance 12 means the physical distance from the screen 11 at the synchronized camera 10 position, and the first distance 12 is the real camera 10-1 or the virtual camera. It can be used in the process of adjusting the focus of at least one of (10-2), and a detailed description will be provided later with reference to FIGS. 6A and 6B.
  • the screen 11 is output based on the synchronized camera, and a virtual environment or an object of the virtual environment can be output.
  • a virtual environment or an object of the virtual environment can be output.
  • an image of a 3D modeled virtual environment may be displayed on the screen 11.
  • an image of a virtual environment in which an actual beach in Thailand is 3D modeled can be displayed on the screen 11, and rocks on the beach (objects of the virtual environment) can be displayed.
  • the real-time movement of the synchronized camera 10 can be simulated and output on the screen 11.
  • the real-time movement of the synchronized camera 10 can be obtained through camera position information and movement information that can be obtained through a real-time camera tracking module, and the screen 11 has a virtual An image of the environment and a simulated real-time movement of the synchronized camera 10 may be synthesized and output. For example, if camera position information and movement information are obtained from the synchronized camera 10 from the perspective of looking up at the sky from the floor, the picture is taken from the perspective of looking up at the sky from the floor in a virtual environment in which a beach in Thailand is 3D modeled. The image may be output on the screen 11.
  • the process of outputting an image on the screen 11 involves transmitting camera position information and movement information acquired through a real-time camera tracking module to a 3D renderer, and using the 3D renderer, an image of a virtual environment and an image of the virtual environment are displayed on the screen 11.
  • An image simulating the real-time movement of the synchronized camera 10 may be synthesized and output.
  • the virtual environment may refer to a specific environment or situation that is similar to reality but is not real, created through artificial technology using computers, etc., and may be implemented through 3D modeling (e.g., real-time 3D rendering, NeRF, etc.) It can be implemented through etc.
  • the 3D renderer may refer to a device or program that performs system graphics processing to display realistic shapes by adding three-dimensional effect to colors, textures, or shadows in an image.
  • the world space of a 3D renderer can be expressed in a three-dimensional coordinate system.
  • each element of the 3D renderer eg, lighting, camera, 3D model, etc.
  • each element is placed in world space (which can be expressed in 3D coordinates). You can.
  • elements such as lighting, cameras, 3D models, etc. are configured in the world space, and visual effects such as shadows, light, etc. can be calculated and rendered based on the distance and angle difference calculated from each other in the world space.
  • the way light is applied to a 3D model can be calculated using the angle difference between the normal vector of the 3D model surface and the light vector.
  • 1 the distance and angle between the screen model in world space (screen modeled in virtual space) and the camera
  • 2 the distance and angle between the camera and the 3D object (target)
  • 3 the distance between the screen model and the 3D object (target)
  • the appropriate focus of the camera within world space can be calculated.
  • the appropriate focus of the camera within world space can be calculated through comparison through vectors between each element.
  • the method of using a 3D renderer to output an image on the screen 11 is, by way of example, projecting the frustum viewed by the camera in the world space of the 3D renderer onto the location of the screen model in the world space. and outputting the projected screen on the actual screen 11 may be included.
  • the frustum may mean a pyramid-like shape with the top cut in a shape parallel to the floor, and the frustum may mean the shape of the area shown and rendered by the perspective camera.
  • Figure 5 shows the process of selecting an object (21, 22) to be focused on and measuring the focal distance (21-1, 22-1) for the selected object at a synchronized camera position according to an embodiment of the present disclosure.
  • This is a schematic diagram.
  • computing device 100 selects an object to focus on and sets a focal distance 21-1 or 22 for the selected object 21 or 22 at the synchronized camera 10 position. -1) can be measured.
  • the object to be focused on may include at least one of an object 21 in a real environment or an object 22 in a virtual environment displayed on the screen 11.
  • the object 22 in the virtual environment may include depth information.
  • computing device 100 may select a “pillar” placed in front of the screen 11 that is an object 21 in the real environment as an object to focus on, and select the selected “pillar” at the synchronized camera 10 location.
  • the first focal distance 21-1 for the “pillar” can be measured.
  • the first focal distance 21-1 corresponds to a case where the synchronized camera 10 is closer than the first distance 12, which is the distance from the screen 11. In this case, the specific process of adjusting the actual camera focus will be described later with reference to FIG. 6A.
  • the computing device 100 selects a “box”, which is the object 22 of the virtual environment output on the screen 11, as an object to focus on, and the synchronized camera 10 ) can measure the second focal distance 22-2 for the selected “box” at the position.
  • the object 22 of the virtual environment displayed on the screen 11 is not an object that actually exists, but includes an object displayed on the screen 11 with depth information, and the “box” is an object that actually exists. Since this is not the case, the focal length cannot be measured with an actual camera (10-1). However, since the real camera 10-1 and the virtual camera 10-2 are synchronized, the focal length for the selected “box” is determined from the position of the synchronized camera 10 by the real camera 10-2.
  • the second focal distance 22-1 may correspond to the focal length measured by the actual camera 10-1 for a cube with each edge having a length of 1 m, placed at a distance of 15 m from the location of the synchronized camera 10.
  • FIG. 6A is a schematic diagram illustrating a process for adjusting the focus of an actual camera when the focal distance is less than or equal to a first distance according to an embodiment of the present disclosure.
  • the computing device 100 may select an object 21 in the real environment as an object to focus on, measure the first focal distance 21-1, and measure the first focal distance 21-1.
  • the focal distance 21-1 is less than or equal to the first distance 12 since it is an object in front of the screen 11.
  • the computing device 100 may adjust the focus 30-1 of the actual camera.
  • the computing device 100 adjusts the focus 30-2 of the virtual camera among the synchronized cameras 10 to the foreground and the focus 30-1 of the real camera. ) can be adjusted to the target 21 in the real environment.
  • the real camera When the depth of field is shallow, the image of the virtual camera also has a shallow depth of field, so when captured with a real camera, the output with a shallow depth of field is made shallow again, causing a problem in which the wrong depth of field may be applied to the output screen 11. You can. Accordingly, when the first focal distance 21-1 is less than or equal to the first distance 12, the computing device 100 adjusts the focus 30-2 of the virtual camera to the foreground and the focus of the real camera.
  • FIG. 6B is a schematic diagram illustrating a process for adjusting the focus of a virtual camera when the focal distance exceeds a first distance according to an embodiment of the present disclosure.
  • the computing device 100 selects an object 22 in the virtual environment displayed on the screen 11 as an object to focus on and measures the second focal distance 22-1.
  • the measured second focal distance 22-1 exceeds the first distance 12 because it is an object behind the screen 11.
  • the computing device 100 may adjust the focus 40-2 of the virtual camera.
  • the computing device 100 fixes the focus 40-1 of the real camera among the synchronized cameras 10 to the screen 11, and fixes the focus 40-1 of the virtual camera 10 to the screen 11. (40-2) can be adjusted for the object 22 in the virtual environment. Since the object 22 of the virtual environment displayed on the screen 11 is not an object that actually exists, the focus 40-1 of the actual camera cannot be set beyond the screen 11.
  • the computing device 100 fixes the focus of the real camera 10-1 on the screen 11, By adjusting only the focus of the virtual camera 10-2 to the object 22 in the virtual environment, the real camera 10-1 cannot focus on the object 22 in the virtual environment. There is a technological effect that can solve the problem.
  • the computing device 100 operates in a state in which the virtual camera 10-2 and the real camera 10-1 are synchronized, as described with reference to FIGS. 6A and 6B, 1 with the synchronized camera 10.
  • adjustment of the focus of at least one of the real camera 10-1 or the virtual camera 10-2 may be performed in conjunction with a joystick or other physical control device, but this is only an example and is not limited thereto. No.
  • Data structure can refer to the organization, management, and storage of data to enable efficient access and modification of data.
  • Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time).
  • a data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device).
  • a data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.
  • Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure.
  • a linear data structure may be a structure in which only one piece of data is connected to another piece of data.
  • Linear data structures may include List, Stack, Queue, and Deque.
  • a list can refer to a set of data that has an internal order.
  • the list may include a linked list.
  • a linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data.
  • a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list.
  • a stack may be a data listing structure that allows limited access to data.
  • a stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure.
  • Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out.
  • a queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later.
  • a deck can be a data structure that can process data at both ends of the data structure.
  • a non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data.
  • Nonlinear data structures may include graph data structures.
  • a graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices.
  • Graph data structure may include a tree data structure.
  • a tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.
  • Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning.
  • a data structure containing a neural network may include any of the components disclosed above.
  • the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be configured to include all or any combination of loss functions for learning.
  • a data structure containing a neural network may include any other information that determines the characteristics of the neural network.
  • the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above.
  • Computer-readable media may include computer-readable recording media and/or computer-readable transmission media.
  • a neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons.
  • a neural network consists of at least one node.
  • the data structure may include data input to the neural network.
  • a data structure containing data input to a neural network may be stored in a computer-readable medium.
  • Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed.
  • Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing.
  • Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing.
  • the above-described data structure is only an example and the present disclosure is not limited thereto.
  • the data structure may include the weights of the neural network. (In this specification, weights and parameters may be used with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium.
  • a neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined.
  • the above-described data structure is only an example and the present disclosure is not limited thereto.
  • the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed.
  • Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle.
  • the above-described data structure is only an example and the present disclosure is not limited thereto.
  • the data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process.
  • Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used.
  • Computing devices can transmit and receive data over a network by serializing data structures.
  • Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization.
  • the data structure including the weights of the neural network is not limited to serialization.
  • the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included.
  • computing device resources e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree.
  • the data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium.
  • a hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers).
  • the above-described data structure is only an example and the present disclosure is not limited thereto.
  • FIG. 7 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.
  • program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • routines programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types.
  • the described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote memory storage devices.
  • Computers typically include a variety of computer-readable media.
  • Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media.
  • Computer-readable media may include computer-readable storage media and computer-readable transmission media.
  • Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media.
  • Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.
  • a computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media.
  • modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal.
  • computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.
  • System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104.
  • Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.
  • System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures.
  • System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112.
  • the basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines.
  • RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.
  • Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown).
  • HDD hard disk drive
  • FDD magnetic floppy disk drive
  • optical disk drive 1120 e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs.
  • Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to.
  • the interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.
  • drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like.
  • drive and media correspond to storing any data in a suitable digital format.
  • computer-readable media refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those of ordinary skill in the art would also recognize zip drives, magnetic cassettes, flash memory cards, and cartridges. It will be appreciated that other types of computer-readable media may also be used in the exemplary operating environment, and that any such media may contain computer-executable instructions for performing the methods of the present disclosure. .
  • a number of program modules may be stored in the drive and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.
  • a user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140.
  • Other input devices may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc.
  • input device interface 1142 which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.
  • a monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146.
  • computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.
  • Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications.
  • Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102.
  • the logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154.
  • LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.
  • computer 1102 When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156.
  • the computer 1102 When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as via the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142.
  • program modules described for computer 1102, or portions thereof may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.
  • Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.
  • wireless communications such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag.
  • PDA portable data assistant
  • Wi-Fi Wireless Fidelity
  • Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a cell tower.
  • Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections.
  • Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet).
  • Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .
  • the various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques.
  • article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device.
  • computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.).
  • various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Optics & Photonics (AREA)
  • Studio Devices (AREA)

Abstract

Selon un mode de réalisation, la présente invention concerne un procédé, mis en oeuvre par un ou plusieurs processeur(s) d'un dispositif informatique, pour mettre en oeuvre le mouvement d'une caméra. Le procédé peut comprendre les étapes suivantes: la synchronisation d'une caméra virtuelle et d'une caméra réelle; la mesure d'une première distance jusqu'à un écran à une position de caméra synchronisée; la sélection d'une cible à focaliser et la mesure d'une distance focale pour la cible sélectionnée à la position de caméra synchronisée; et le réglage d'une mise au point de la caméra réelle et/ou de la caméra virtuelle sur la base de la première distance et de la distance focale.
PCT/KR2023/010387 2022-10-20 2023-07-19 Procédé de mise en oeuvre de mouvement de caméra au moyen d'une caméra virtuelle WO2024085366A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR1020220135656A KR102559913B1 (ko) 2022-10-20 2022-10-20 가상 카메라를 이용한 카메라 움직임 구현 방법
KR10-2022-0135656 2022-10-20

Publications (1)

Publication Number Publication Date
WO2024085366A1 true WO2024085366A1 (fr) 2024-04-25

Family

ID=87427524

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2023/010387 WO2024085366A1 (fr) 2022-10-20 2023-07-19 Procédé de mise en oeuvre de mouvement de caméra au moyen d'une caméra virtuelle

Country Status (2)

Country Link
KR (2) KR102559913B1 (fr)
WO (1) WO2024085366A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117528236A (zh) * 2023-11-01 2024-02-06 神力视界(深圳)文化科技有限公司 虚拟摄像机的调整方法和装置
CN117527992B (zh) * 2023-11-06 2024-06-21 中影电影数字制作基地有限公司 一种空间模拟拍摄的摄影机校正方法及系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170100371A (ko) * 2016-02-25 2017-09-04 연세대학교 산학협력단 스테레오 삼차원 영상 콘텐츠 디스플레이 제작 및 재생 방법
KR20180052496A (ko) * 2016-11-10 2018-05-18 주식회사 다림비젼 가상 공연 스튜디오 시스템
KR102001255B1 (ko) * 2013-02-28 2019-07-17 고려대학교 산학협력단 스테레오 영상 획득 및 시청 연동 시스템
KR102371031B1 (ko) * 2021-08-13 2022-03-07 주식회사 비브스튜디오스 버추얼 프로덕션의 영상 촬영을 위한 장치, 시스템, 방법 및 프로그램
KR102409188B1 (ko) * 2021-11-09 2022-06-15 주식회사 아진엑스텍 증강현실 영상 출력 제어 방법 및 장치

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108540731A (zh) * 2018-04-17 2018-09-14 北京艾沃次世代文化传媒有限公司 实拍摄影机与虚拟场景实时同步显示方法
CN112040092B (zh) * 2020-09-08 2021-05-07 杭州时光坐标影视传媒股份有限公司 一种实时虚拟场景led拍摄系统及方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102001255B1 (ko) * 2013-02-28 2019-07-17 고려대학교 산학협력단 스테레오 영상 획득 및 시청 연동 시스템
KR20170100371A (ko) * 2016-02-25 2017-09-04 연세대학교 산학협력단 스테레오 삼차원 영상 콘텐츠 디스플레이 제작 및 재생 방법
KR20180052496A (ko) * 2016-11-10 2018-05-18 주식회사 다림비젼 가상 공연 스튜디오 시스템
KR102371031B1 (ko) * 2021-08-13 2022-03-07 주식회사 비브스튜디오스 버추얼 프로덕션의 영상 촬영을 위한 장치, 시스템, 방법 및 프로그램
KR102409188B1 (ko) * 2021-11-09 2022-06-15 주식회사 아진엑스텍 증강현실 영상 출력 제어 방법 및 장치

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
SEOUL XR VERIFICATION CENTER: "[2022 DMC XR Media Forum][KBS Team Leader Seungjun Kim][VIVE STUDIOS CTO Gwanghee Lee] Two perspectives on XR: Concerns and solutions of broadcasting stations and production companies", XP093169050, Retrieved from the Internet <URL:https://www.youtube.com/watch?v=gs2WHBSAtz8> *

Also Published As

Publication number Publication date
KR102559913B1 (ko) 2023-07-26
KR20240055628A (ko) 2024-04-29

Similar Documents

Publication Publication Date Title
WO2024085366A1 (fr) Procédé de mise en oeuvre de mouvement de caméra au moyen d&#39;une caméra virtuelle
WO2021261696A1 (fr) Segmentation d&#39;instances d&#39;objets visuels à l&#39;aide d&#39;une imitation de modèle spécialisé de premier plan
WO2019039757A1 (fr) Dispositif et procédé de génération de données d&#39;apprentissage et programme informatique stocké dans un support d&#39;enregistrement lisible par ordinateur
WO2021261825A1 (fr) Dispositif et procédé de génération de données météorologiques reposant sur l&#39;apprentissage automatique
WO2024080791A1 (fr) Procédé de génération d&#39;ensemble de données
WO2024117708A1 (fr) Procédé de conversion d&#39;image faciale à l&#39;aide d&#39;un modèle de diffusion
WO2023027279A1 (fr) Procédé de prédiction de la liaison ou non d&#39;un atome à l&#39;intérieur d&#39;une structure chimique à une kinase
KR20240028909A (ko) 신경망 모델을 위한 학습 데이터 생성 방법
KR102546175B1 (ko) 얼굴 변환을 수행하는 신경망 모델을 학습시키기 위한 방법
WO2023101417A1 (fr) Procédé permettant de prédire une précipitation sur la base d&#39;un apprentissage profond
WO2024143909A1 (fr) Procédé de conversion d&#39;image en étapes en prenant en considération des changements d&#39;angle
WO2021251691A1 (fr) Procédé de détection d&#39;objet à base de rpn sans ancrage
KR102565420B1 (ko) 가상 제작 환경에서 객체 분할을 수행하는 방법
KR102606616B1 (ko) 관객의 위치에 의존하여 예술품을 동적으로 표시하는 방법
KR102635768B1 (ko) 데이터셋을 생성하기 위한 방법
WO2023075351A1 (fr) Procédé d&#39;apprentissage d&#39;intelligence artificielle pour robot industriel
WO2023219237A1 (fr) Procédé basé sur l&#39;intelligence artificielle pour évaluer une doublure
WO2024143907A1 (fr) Procédé pour entraîner un modèle de réseau neuronal pour convertir une image en utilisant des images partielles
WO2023008811A2 (fr) Procédé de reconstruction d&#39;image de visage masqué à l&#39;aide d&#39;un modèle de réseau neuronal
KR102642665B1 (ko) 가상 카메라를 조정하는 방법
WO2024147462A1 (fr) Procédé permettant d&#39;améliorer la qualité de transformation de visage à l&#39;aide d&#39;un mélange d&#39;images
WO2024019373A1 (fr) Procédé et dispositif permettant de mettre à jour une carte tridimensionnelle
WO2023224457A1 (fr) Procédé d&#39;obtention d&#39;un point caractéristique d&#39;une carte de profondeur
KR102606617B1 (ko) 확산 모델을 이용한 동영상 생성 방법
WO2023224456A1 (fr) Procédé de génération d&#39;ensemble de données

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23879950

Country of ref document: EP

Kind code of ref document: A1