KR102565420B1 - Method for performing object segmentation in virtual production environment - Google Patents

Abstract

A method for performing object segmentation in a virtual production environment, performed by a computing device, according to an embodiment of the present disclosure is disclosed. The method may include acquiring an image of a background displayed on a display device and a real object photographed; transmitting the modified background to the display device so that a modified area surrounding the real object is created; obtaining a modified image in which the modified background displayed on the display device and the real object are photographed; and separating the image of the real object by utilizing the corrected area.

Description

How to perform object segmentation in virtual production environment {METHOD FOR PERFORMING OBJECT SEGMENTATION IN VIRTUAL PRODUCTION ENVIRONMENT}

The present invention relates to a method for performing object segmentation, and more particularly to a technique for performing object segmentation in a virtual production environment.

In computer vision, an object segmentation technology based on a monocular camera cannot expect accurate results without depth information such as lidar or stereo cameras. For precise separation, object segmentation is sometimes performed manually in the post-editing process after shooting, and foreground, background, objects (eg, people) are separated using a chromakey technique.

However, chroma key shooting requires a compositing process after shooting, and the process takes a lot of work time. Recently, real-time images or graphics are visualized and photographed using LED walls instead of chroma key, but the accuracy of object segmentation implementation is low.

Korean Patent Registration No. 10-0661528 (2006.12.19) discloses an adaptive chroma key synthesis apparatus and method.

The present disclosure aims to provide a method for performing object segmentation in a virtual authoring environment.

On the other hand, the technical problem to be achieved by the present disclosure is not limited to the above-mentioned technical problem, and may include various technical problems within a range apparent to those skilled in the art from the contents to be described below.

A method performed by a computing device according to an embodiment of the present disclosure for realizing the above object is disclosed. The method may include acquiring an image of a background displayed on a display device and a real object photographed; transmitting the modified background to the display device so that a modified area surrounding the real object is created; obtaining a modified image in which the modified background displayed on the display device and the real object are photographed; and separating the image of the real object by utilizing the corrected area.

In one embodiment, the modified background may include a partial background area of a specific color for implementing the modified area; and other background areas identical to the original background except for the partial background area.

In one embodiment, the step of transmitting the modified background to the display device so that a modified area surrounding the real object is created, such that a bounding box area surrounding the real object is created, the display device Sending the modified background to the device.

In an embodiment, the separating the image of the real object by using the modified area may include filling the inside of the bounding box with a specific color; generating a mask image corresponding to the image of the real object based on the specific color of the bounding box area; and separating the image of the real object by utilizing the mask image.

In an embodiment, the separating the image of the real object by using the mask image may include generating an object alpha map using the mask image.

In one embodiment, the method may further include, after separating the image of the real object, implementing depth occlusion related to the real object.

In one embodiment, implementing depth occlusion related to the real object may include obtaining depth map scale information by utilizing actual size information of the real object and intrinsic information of a photographing device. Acquisition steps may be included.

In one embodiment, the display device includes an LED panel, and the real object may be photographed together with the LED panel.

A computer program stored in a computer readable storage medium is disclosed according to an embodiment of the present disclosure for realizing the above object. When the computer program is executed on one or more processors, the following operations are performed to perform object segmentation in a virtual production environment, which include: a background displayed on a display device and a real object Obtaining a photographed image; transmitting a modified background to the display device so that a modified area surrounding the real object is created; acquiring a modified image of the real object and the modified background displayed on the display device; and separating the image of the real object by utilizing the corrected area.

A computing device according to an embodiment of the present disclosure for realizing the above object is disclosed. The device may include at least one processor; Memory; and a network unit, wherein the at least one processor obtains a photographed image of a background and a real object displayed on a display device; transmitting the modified background to the display device using the network unit so that a modified area surrounding the real object is created; obtaining a modified image in which a modified background displayed on the display device and the real object are photographed; In addition, it may be configured to separate the image of the real object by utilizing the modified area.

The present disclosure does not use a specific physical screen such as a chroma-key to implement a virtual production environment or depth occlusion technology, but only input camera images (eg, background A high-level object segmentation method can be provided even with the output screen and the camera image that captured the real object.

On the other hand, the effects of the present disclosure are not limited to the effects mentioned above, and various effects may be included within a range apparent to those skilled in the art from the contents to be described below.

1 is a block diagram of a computing device for performing object segmentation in a virtual production environment according to an embodiment of the present disclosure.
2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.
3 is a diagram schematically illustrating an example of obtaining an image in which a background displayed on a display device and a real object are photographed according to an embodiment of the present disclosure.
4 is a diagram schematically illustrating an example of generating a bounding box area surrounding a real object to create a modified area according to an embodiment of the present disclosure.
5 is a diagram schematically illustrating an example of transmitting a modified area and a modified background surrounding a real object to a display device according to an embodiment of the present disclosure.
6 is a diagram schematically illustrating an operation of generating a mask image corresponding to an image of a real object according to an embodiment of the present disclosure.
7 is a diagram schematically illustrating an operation of generating an object alpha map using a mask image according to an embodiment of the present disclosure.
8 is a diagram schematically illustrating an operation of implementing depth occlusion related to a real object according to an embodiment of the present disclosure.
9 is a flowchart of a method of performing object segmentation in a virtual production environment according to an embodiment of the present disclosure.
10 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific details.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an execution of software. For example, a component may be, but is not limited to, a procedure, processor, object, thread of execution, program, and/or computer running on a processor. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component may be distributed between two or more computers. Also, these components can execute from various computer readable media having various data structures stored thereon. Components may be connected, for example, via signals with one or more packets of data (e.g., data and/or signals from one component interacting with another component in a local system, distributed system) to other systems and over a network such as the Internet. data being transmitted) may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or clear from the context, “X employs A or B” is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or, if X uses both A and B, "X uses either A or B" may apply to either of these cases. Also, the term "and/or" as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the features and/or components are present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Also, unless otherwise specified or where the context clearly indicates that a singular form is indicated, the singular in this specification and claims should generally be construed to mean "one or more".

In addition, the term “at least one of A or B” should be interpreted as meaning “when only A is included”, “when only B is included”, and “when A and B are combined”.

Those skilled in the art will further understand that the various illustrative logical blocks, components, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure. The general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present invention is not limited to the embodiments presented herein. The present invention is to be accorded the widest scope consistent with the principles and novel features set forth herein.

In the present disclosure, network functions, artificial neural networks, and neural networks may be used interchangeably.

1 is a block diagram of a computing device for performing object segmentation in a virtual production environment according to an embodiment of the present disclosure.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present disclosure, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute 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 include one or more cores, and includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. unit), data analysis, and processors for deep learning. The processor 110 may read a computer program stored in the memory 130 and process data 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. The processor 110 is used for neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed. At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in an embodiment of the present disclosure, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present disclosure may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present disclosure, 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 .

According to an embodiment of the present disclosure, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg, SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) -Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The above description of the memory is only an example, and the present disclosure is not limited thereto.

The network unit 150 according to an embodiment of the present disclosure includes a Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL ( Various wired communication systems such as Very High Speed DSL), Universal Asymmetric DSL (UADSL), High Bit Rate DSL (HDSL), and Local Area Network (LAN) may be used.

In addition, the network unit 150 presented in this specification includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), SC-FDMA ( Single Carrier-FDMA) and other systems.

In the present disclosure, the network unit 150 may be configured regardless of its communication mode, such as wired and wireless, and may include a local area network (LAN), a personal area network (PAN), and a wide area network (WAN: It can be composed of various communication networks such as Wide Area Network). In addition, the network may be the known World Wide Web (WWW), or may use a wireless transmission technology used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth.

The techniques described herein may be used in the networks mentioned above as well as other networks.

2 is a schematic diagram illustrating a network function according to an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of data of the output node may be determined based on data input to the input node. Here, a link interconnecting an input node and an output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values of the links, the two neural networks may be recognized as different from each other.

A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may constitute a layer. Some of the nodes constituting the neural network may form one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in a neural network may be defined in a method different from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the neural network. Alternatively, in a relationship between nodes based on a link in a neural network, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the neural network. Also, the hidden node may refer to nodes constituting the neural network other than the first input node and the last output node.

In the neural network according to an embodiment of the present disclosure, 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 the number of nodes progresses from the input layer to the hidden layer. can In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of the input layer may be less than the number of nodes of the output layer and the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure is 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 the number of nodes increases from the input layer to the hidden layer. can A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted boltzmann machines (RBMs). machine), a deep belief network (DBN), a Q network, a U network, a Siamese network, a Generative Adversarial Network (GAN), and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to dimensions after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease 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, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

The neural network may be trained using at least one of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning of the neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

A neural network can be trained in a way that minimizes output errors. In the learning of the neural network, the learning data is repeatedly input into the neural network, the output of the neural network for the training data and the error of the target 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. It is a process of updating the weight of each node of the neural network by backpropagating in the same direction. In the case of teacher learning, the learning data in which the correct answer is labeled is used for each learning data (ie, the labeled learning data), and in the case of comparative teacher learning, the correct answer may not be labeled in each learning data. That is, for example, learning data in the case of teacher learning regarding data classification may be data in which each learning data is labeled with a category. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network and a label of the training data. As another example, in the case of comparative history learning for data classification, an error may be calculated by comparing input learning data with a neural network output. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate may be used in the early stage of neural network training to increase efficiency by allowing the neural network to quickly obtain a certain level of performance, and a low learning rate may be used in the late stage to increase accuracy.

In neural network learning, generally, training data can be a subset of real data (ie, data to be processed using the trained neural network), and therefore, errors for training data are reduced, but errors for real data are reduced. There may be incremental learning cycles. Overfitting is a phenomenon in which errors for actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that has learned a cat by showing a yellow cat does not recognize that it is a cat when it sees a cat other than yellow may be a type of overfitting. Overfitting can act as a cause of increasing the error of machine learning algorithms. Various optimization methods can be used to prevent such overfitting. To prevent overfitting, methods such as increasing the training data, regularization, inactivating some nodes in the network during learning, and using a batch normalization layer should be applied. can

According to an embodiment of the present disclosure, described below, virtual production is a camera, a CG space, and a display device (eg, an LED panel, an LED wall) are synchronized and synthesized in real time, and real-time according to the movement of the camera. Graphics are projected onto a display device (eg, LED panel, LED wall) to create a scene as if it were in the field. Images displayed on display devices (eg, LED panels, LED walls) are real-time based contents using the Unreal program. This has the advantage that the main space (eg, environment, background), characters, and props constituting the content can be expanded and utilized almost simultaneously to works of various genres beyond the existing limitations of being used only for the corresponding work.

According to an embodiment of the present disclosure, the processor 110 may perform object segmentation in a virtual production environment. Illustratively, the processor 110 primarily identifies object segmentation information based on extracting a specific color gamut for object segmentation in a display device (eg, an LED wall), and based on the extracted color gamut After generating the masking image, it is possible to finally improve the accuracy of the object segmentation result by using an object alpha map. In addition, the processor 110 does not require a specific physical screen such as a chroma key to implement virtual production or depth occlusion technology, and an input camera image (eg, a background is output It is possible to perform object segmentation only with the screen and the camera image that captured the real object.

On the other hand, according to an embodiment of the present disclosure, the processor 110 separates the image of the real object by utilizing a "modified area (created by modifying a partial area of the background displayed on the display device)", Object segmentation can be performed more easily than when the object is overlapped with the background image (in other words, the border of the object and the border of the background are in direct contact), and the accuracy of object segmentation is further improved. can improve That is, the processor 110, rather than performing object segmentation on an image in which a background formed in various colors and an object are harmonized, a modified image in which a background partially modified from an original background (a background formed in various colors) and a real object are captured The accuracy of object segmentation can be further improved by performing object segmentation by obtaining .

More specific details of the configuration mentioned above will be described in detail with reference to FIGS. 3 to 8 below.

As described above, according to an embodiment of the present disclosure, the processor 110 separates the image of the real object by utilizing the modified area, so that the object overlaps the background image (the object and the background are separated from the boundary). sharing state), object segmentation can be performed more easily than in the case of performing object segmentation, and the accuracy of object segmentation can be further improved. In other words, rather than performing object segmentation on an image in which the background formed in various colors and the object are combined, the processor 110 uses a modified background partially modified from the original background (background formed in various colors) and a real object captured. By obtaining an image and performing object segmentation, accuracy of object segmentation may be further improved.

3 is a diagram schematically illustrating an example of obtaining an image in which a background displayed on a display device and a real object are photographed according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may acquire an image in which the background A and the real object B displayed on the display device are photographed. In addition, the processor 110 may obtain a captured image of the background A and the real object B displayed on the display device from a capture board connected to a photographing device (eg, a camera). Here, the display device may include an LED panel. Also, the real object B may be photographed together with the LED panel. Illustratively, referring to FIG. 3 , the processor 110 may acquire an image of a real object B together with a background A output on a display device (eg, an LED wall) located inside a virtual studio. there is. In addition, the processor 110 synchronizes the photographing device (eg, camera) and the display device (eg, LED panel), and displays real-time graphics according to the movement of the photographing device (eg, camera) to a large display device (eg, LED panel). ) to obtain realistic images. In addition, the processor 110 can adjust natural lighting, precise angle of reflection, and environmental changes such as the sun, clouds, and atmospheric conditions in real time during filming. A photographed image may be acquired.

4 is a diagram schematically illustrating an example of generating a bounding area surrounding a real object to create a modified area according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may create a bounding area surrounding the real object B before creating the modified area. Here, the bounding area may be implemented in various forms formed to surround the real object B. For example, the bounding area may be formed in the shape of a bounding box area, or may be formed in various shapes such as a circle, polygon, irregular shape, etc. As an example, referring to FIG. 4 , the processor 110 may generate a bounding box area C to include the real object B in the captured image of the background A and the real object B displayed on the display device. can In other words, the processor 110 may classify and extract an area for which object segmentation is to be performed as a bounding box area.

5 is a diagram schematically illustrating an example of transmitting a modified area and a modified background surrounding a real object to a display device according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may fill the inside of the bounding box area C, which is an area surrounding an object, with a specific color, as described above with reference to FIG. 4 . For example, the modified area may include a bounding box area (C), which is an area surrounding an object, and a partial background area (D) filled with a specific color inside the bounding box. Also, the processor 110 may transmit the modified background to the display device so that a modified area surrounding the real object is created. Here, the modified background may include a partial background area (D) of a specific color for realizing the modified area, and a remaining area (E) identical to the original background except for the partial background area (D). More specifically, referring to FIG. 5 , the processor 110 creates a modified area surrounding the real object by changing the bounding box area C previously generated in FIG. 4 to a partial background area D of a specific color. can do. Also, the processor 110 may transmit the modified background to the display device using the network unit 150 . That is, the display device may output a modified background, and the processor 110 may cause the modified background displayed on the display device (eg, an LED panel) and a real object to be photographed, through which a modified image may be obtained. . In addition, the processor 110 transmits the modified background to a display device (eg, an LED panel), and performs object segmentation from the modified background displayed on the display device and the modified image in which the real object is photographed, so that various colors are mixed. Object segmentation can be performed more conveniently than in the case of object segmentation in the background.

6 is a diagram schematically illustrating an operation of generating a mask image corresponding to an image of a real object according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may separate the image of the real object by utilizing the modified area. Illustratively, referring to FIG. 6 , the processor 110 generates a mask image F corresponding to an image of a real object based on a modified region in which the inside of the bounding box region C is changed to a specific color. can create In other words, the processor 110 may generate a mask image F corresponding to an image of a real object included in the partial background area D. In this case, the processor 110 may perform a masking technique to generate a mask image F corresponding to an image of a real object from an image in which a real object and a display device (eg, an LED panel) are captured together. . Also, the processor 110 may extract a minimum specific color region through the bounding box region C and generate a masking image for the real object B based on the extracted region. Also, the processor 110 may separate the image of the real object B by utilizing the mask image F. Specifically, the processor 110 may separate only the image of the real object B by utilizing the mask image F in the area where the real object and the display device (eg, the LED panel) are photographed together.

7 is a diagram schematically illustrating an operation of generating an object alpha map using a mask image according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may generate an object alpha map using the mask image F. The alpha map recognizes the area corresponding to the first color as transparent in the image divided into the first color and the second color, and displays an image corresponding to the actual image for the area corresponding to the second color. will be. Referring to FIG. 7 as an example, the processor 110 displays an image of a real object B corresponding to a real image in an area corresponding to a mask image F, and a transparent image in an area excluding the real object B. You can create an object alpha map that represents .

8 is a diagram schematically illustrating an operation of implementing depth occlusion related to a real object according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, the processor 110 may implement depth occlusion related to the real object after separating the image of the real object. Occlusion is the effect of blocking light on an object. Referring to FIG. 8 , the processor 110 implements depth occlusion to determine the depths of the front and rear surfaces of the real object B.

In addition, the processor 110 may obtain depth map scale information by utilizing actual size information (eg, height, joint, etc.) of the real object B and intrinsic information of the photographing device. can For example, the intrinsic information of the imaging device may include, but is not limited to, a focal length, a principal point, and a screw coefficient. Meanwhile, the processor 110 may improve a 3D effect by obtaining depth map scale information.

Also, the processor 110 may predict the position of a keypoint for an object based on an object alpha map. For example, if the object is a person, the processor 110 may predict positions of key points related to the person's body. More specifically, the processor 110 may predict positions of body keypoints such as nose, left_eye_inner, right_ear, left_shoulder, right_elbow, right_thumb, left_anckle, left_foot_index, and the like.

In addition, the processor 110 may accurately perform object segmentation by utilizing these additional information even when the upper body or a part of the body, rather than the entire body of the object, is the target of object segmentation. For example, the processor 110 generates an object alpha map, and then utilizes size information (eg, height, joint, etc.) of the real object B to use the upper body or body (not the entire body of the object). Object segmentation can be accurately performed even when a part of is the object of object segmentation.

9 is a flowchart of a method of performing object segmentation in a virtual production environment according to an embodiment of the present disclosure.

The method of performing object segmentation in the virtual production environment shown in FIG. 9 may be performed by the computing device 100 described above. Even if not mentioned in detail below, the above description of the computing device 100 may be equally applied to a description of a method of performing object segmentation in a virtual production environment.

Referring to FIG. 9 , a method for performing object segmentation in a virtual production environment according to an embodiment of the present disclosure includes obtaining an image in which a background displayed on a display device and a real object are photographed (S110), and the real object Transmitting the modified background to the display device so that a surrounding modified area is generated (S120), obtaining a modified image of the real object and the modified background displayed on the display device (S130), and A step of separating the image of the real object by utilizing the corrected area (S140) may be included, and various steps other than these steps may be included. Also, a method of performing object segmentation in a virtual production environment according to an embodiment of the present disclosure may be performed by the computing device 100 .

The step S110 is a step for obtaining an image in which the background displayed on the display device and the real object are photographed. For example, the display device may include an LED panel. Also, a real object may be photographed together with the LED panel.

The step S120 is a step of transmitting a modified background to the display device so that a modified area surrounding the real object is created. Here, the modified background includes a partial background area of a specific color for realizing the modified area; and other background areas identical to the original background except for the partial background. The step S120 may include transmitting the modified background to the display device so that a bounding box area surrounding the real object is created.

The step S130 is a step of obtaining a modified image in which a modified background displayed on the display device and the real object are photographed.

The step S140 is a step of separating the image of the real object by utilizing the modified area.

Meanwhile, in the method for performing object segmentation in a virtual production environment according to an embodiment of the present disclosure, in addition to the steps S110 to S140, after dividing the image of the real object, depth occlusion related to the real object A step of implementing occlusion may be further included. In addition, implementing depth occlusion related to the real object may include obtaining depth map scale information by utilizing actual size information of the real object and intrinsic information of a photographing device. can include

The steps mentioned in the foregoing description may be further divided into additional steps or combined into fewer steps, depending on the implementation of the present disclosure. Also, some steps may be omitted if necessary, and the order of steps may be changed.

According to an embodiment of the present disclosure, a computer readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data that enables efficient access and modification of data. Data structure may refer to the organization of data to solve a specific problem (eg, data retrieval, data storage, data modification in the shortest time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. A logical relationship between data elements may include a connection relationship between user-defined data elements. A physical relationship between data elements may include an actual relationship between data elements physically stored in a computer-readable storage medium (eg, a persistent storage device). The data structure may specifically include a set of data, a relationship between data, and a function or command applicable to the data. Through an effectively designed data structure, a computing device can perform calculations while using minimal resources of the computing device. Specifically, the computing device can increase the efficiency of operation, reading, insertion, deletion, comparison, exchange, and search through an effectively designed data structure.

The data structure can be divided into a linear data structure and a non-linear data structure according to the shape of the data structure. A linear data structure may be a structure in which only one data is connected after one data. Linear data structures may include lists, stacks, queues, and decks. A list may refer to a series of data sets in which order exists internally. The list may include a linked list. A linked list may be a data structure in which data are connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer can contain information about connection to the next or previous data. A linked list can be expressed as a singly linked list, a doubly linked list, or a circular linked list depending on the form. A stack can be a data enumeration structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (eg, inserted or deleted) at only one end of the data structure. The data stored in the stack may be a LIFO-Last in First Out (Last in First Out) data structure. A queue is a data listing structure that allows limited access to data, and unlike a stack, it can be a data structure (FIFO-First in First Out) in which data stored later comes out later. A deck can be a data structure that can handle data from either end of the data structure.

The nonlinear data structure may be a structure in which a plurality of data are connected after one data. The non-linear data structure may include a graph data structure. A graph data structure can be defined as a vertex and an edge, and an edge can include a line connecting two different vertices. A graph data structure may include a tree data structure. The tree data structure may be a data structure in which one path connects two different vertices among a plurality of vertices included in the tree. That is, it may be a data structure that does not form a loop in a graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Hereinafter, a neural network is unified and described. The data structure may include a neural network. And the data structure including the neural network may be stored in a computer readable medium. The data structure including the neural network may also include 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 obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network It may include a loss function for learning of . A data structure including a neural network may include any of the components described above. That is, 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 obtained from the neural network, activation function associated with each node or layer of the neural network, and neural network. It may be configured to include all or any combination thereof, such as a loss function for learning of . In addition to the foregoing configurations, the data structure comprising the neural network may include any other information that determines the characteristics of the neural network. In addition, the data structure may include all types of data used or generated in the computational process of the neural network, but is not limited to the above. A computer readable medium may include a computer readable recording medium and/or a computer readable transmission medium. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. A neural network includes one or more nodes.

The data structure may include data input to the neural network. A data structure including data input to the neural network may be stored in a computer readable medium. Data input to the neural network may include training data input during a neural network learning process and/or input data input to a neural network that has been trained. Data input to the neural network may include pre-processed data and/or data subject to pre-processing. Pre-processing may include a data processing process for inputting data to a neural network. Accordingly, the data structure may include data subject to pre-processing and data generated by pre-processing. The foregoing 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 in the same meaning.) Also, a data structure including weights of a neural network may be stored in a computer readable medium. A neural network may include a plurality of weights. The weight may be variable, and may be changed by a user or an algorithm in order to perform a function desired by the neural network. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. A data value output from an output node may be determined based on the weight. The foregoing data structure is only an example, and the present disclosure is not limited thereto.

As a non-limiting example, the weights may include weights that are varied during neural network training and/or weights for which neural network training has been completed. The variable weight in the neural network learning process may include a weight at the time the learning cycle starts and/or a variable weight during the learning cycle. The weights for which neural network learning has been completed may include weights for which learning cycles have been completed. Accordingly, the data structure including the weights of the neural network may include a data structure including weights that are variable during the neural network learning process and/or weights for which neural network learning is completed. Therefore, it is assumed that the above-described weights and/or combinations of weights are included in the data structure including the weights of the neural network. The foregoing 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 (eg, a memory or a 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 another computing device and later reconstructed and used. A computing device may serialize data structures to transmit and receive data over a network. The data structure including the weights of the serialized neural network may be reconstructed on the same computing device or another computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure for increasing the efficiency of operation while minimizing the resource of the computing device (for example, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree). The foregoing is only an example, and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of the neural network. Also, the data structure including the hyperparameters of the neural network may be stored in a computer readable medium. A hyperparameter may be a variable variable by a user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle iterations, weight initialization (eg, setting the range of weight values to be targeted for weight initialization), hidden unit number (eg, the number of hidden layers and the number of nodes in the hidden layer). The foregoing data structure is only an example, and the present disclosure is not limited thereto.

10 is a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above as being generally embodied by a computing device, those skilled in the art will understand that the present disclosure may be combined with computer-executable instructions and/or other program modules that may be executed on one or more computers and/or may be implemented in hardware and software. It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will understand that the methods of the present disclosure can be applied to single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, and the like ( It will be appreciated that each of these may be implemented with other computer system configurations, including those that may be operative in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, 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, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer readable media may include computer readable storage media and computer readable transmission media. Computer readable storage media are volatile and nonvolatile media, transitory and non-transitory, removable and non-removable media 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 device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store desired information.

A computer readable transmission medium typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Including all information delivery media. The term modulated data signal means a signal that has one or more of its characteristics set or changed so as to encode information within the signal. By way of example, and not limitation, 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.

An exemplary environment 1100 implementing various aspects of the present disclosure is shown including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. 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 processor and other multiprocessor architectures may also be used as the processing unit 1104.

System bus 1108 may be any of several types of bus structures that may additionally be interconnected to a memory bus, a peripheral bus, and a 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 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, or EEPROM, and is a basic set of information that helps transfer information between components within computer 1102, such as during startup. contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also include an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes—a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM) for reading disc 1122 or reading from or writing to other high capacity optical media such as DVDs). The hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to the system bus 1108 by a hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to The interface 1124 for external drive implementation includes at least one or both of USB (Universal Serial Bus) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art can use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible media readable by the computer, such as the like, may also be used in the exemplary operating environment and any such media may include computer executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored on 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 in a variety of commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, a parallel port, IEEE 1394 serial port, game port, USB port, IR interface, may be connected by other interfaces such as the like.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, and the like.

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, handheld computer, microprocessor-based entertainment device, peer device, or other common network node, and generally includes It includes many or all of the components described for, but for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and corporations and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to worldwide computer networks, such as the Internet.

When used in a LAN networking environment, computer 1102 connects to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communications to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, computer 1102 may include a modem 1158, be connected to a communicating computing device on WAN 1154, or establish communications over WAN 1154, such as over the Internet. have other means. A modem 1158, which may be internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored on remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between computers may be used.

Computer 1102 is any wireless device or entity that is deployed and operating in wireless communication, eg, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communication satellites, wireless detectable tags associated with It operates to communicate with arbitrary equipment or places and telephones. This includes at least Wi-Fi and Bluetooth wireless technologies. Thus, the communication may be a predefined structure as in conventional networks or simply an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet without wires. Wi-Fi is a wireless technology, such as a cell phone, that allows such devices, eg, computers, to transmit and receive data both indoors and outdoors, i.e. anywhere within coverage of a base station. Wi-Fi networks use a radio 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 radio bands, for example, at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band) .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields s or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein are electronic hardware, (for convenience) , may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash memory devices (eg, EEPROM, cards, sticks, key drives, etc.), but are not limited thereto. Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of example approaches. Based upon design priorities, it is to be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure. The accompanying method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art of this disclosure, and the general principles defined herein may be applied to other embodiments without departing from the scope of this disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be interpreted in the widest scope consistent with the principles and novel features presented herein.

Claims (10)

A method of performing object segmentation in a virtual production environment, performed by a computing device, comprising:
Obtaining a photographed image of a background displayed on a display device and a real object;
transmitting a modified background to the display device so that a bounding box area surrounding the real object is created, and filling the inside of the bounding box with a specific color;
obtaining a modified image in which the modified background displayed on the display device and the real object are photographed;
separating the image of the real object by utilizing the corrected area; and
obtaining depth map scale information by utilizing actual size information of the real object and intrinsic information of a photographing device, and implementing depth occlusion related to the real object;
including,
The step of separating the image of the real object by utilizing the modified area,
Separating the image of the real object based on the specific color of the bounding box area
including,
method.
According to claim 1,
The modified background,
a partial background area of a specific color for realizing the modified area; and
Except for the partial background area, the rest of the background area is the same as the original background.
including,
method.
delete According to claim 1,
The step of separating the image of the real object by utilizing the modified area,
generating a mask image corresponding to the image of the real object based on the specific color of the bounding box area; and
Separating the image of the real object using the mask image
including,
method.
According to claim 4,
The step of separating the image of the real object by using the mask image,
Generating an object alpha map using the mask image
including,
method.
delete delete According to claim 1,
The display device includes an LED panel,
The real object is photographed together with the LED panel,
method.
A computer program stored on a computer-readable storage medium, which, when executed on one or more processors, performs the following operations for performing object segmentation in a virtual production environment, wherein the Actions are:
Obtaining an image in which the background displayed on the display device and the real object are photographed;
transmitting the modified background to the display device and filling the inside of the bounding box with a specific color so that a bounding box area surrounding the real object is created;
acquiring a modified image of the real object and the modified background displayed on the display device;
separating the image of the real object by utilizing the modified area; and
acquiring depth map scale information by utilizing actual size information of the real object and intrinsic information of a photographing device, and implementing depth occlusion related to the real object;
including,
The operation of separating the image of the real object by utilizing the modified area,
Separating the image of the real object based on the specific color of the bounding box area
including,
A computer program stored on a computer readable storage medium.
A computing device for performing object segmentation in a virtual production environment,
at least one processor;
Memory; and
network department
including,
The at least one processor,
Obtaining a photographed image of a background and a real object displayed on a display device;
transmitting the modified background to the display device using the network unit and filling the inside of the bounding box with a specific color so that a bounding box area surrounding the real object is created;
obtaining a modified image in which a modified background displayed on the display device and the real object are photographed;
separating the image of the real object by utilizing the modified area; and
configured to obtain depth map scale information by utilizing actual size information of the real object and intrinsic information of a photographing device, and implement depth occlusion related to the real object;
The process of separating the image of the real object using the modified area,
Separating the image of the real object based on the specific color of the bounding box area
including,
Device.

Images