Abstract: An apparatus for evaluating the quality of digital human content included in a source input are disclosed. The apparatus comprises a test method generation unit configured to receive test methods including identification information of questions and evaluation methods for the questions, and referring to a pre-stored question list and an evaluation method set, generate at least one of subjective test methods or objective test methods from the test methods. The apparatus comprises an evaluation result acquisition unit configured to obtain subjective evaluation results for the digital human content using the subjective test methods and obtain objective evaluation results for the digital human content using the objective test methods.

Abstract: Disclosed herein is a method for improving the quality and realism of a rendered image. The method includes receiving training data including a real image and a rendered image, generating a low-quality image using the training data, generating a high-quality image using the low-quality image, generating a realistic image using the high-quality image, and training a neural network using an error calculated based on the high-quality image and the realistic image.

Abstract: Disclosed herein are an apparatus and method for guiding multi-view capture. The apparatus for guiding multi-view capture includes one or more processors and an execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive a single-view two-dimensional (2D) image obtained by capturing an image of an object of interest through a camera, generate an orthographic projection image and a perspective projection image for the object of interest from the single-view 2D image using an image conversion parameter that is previously learned from multi-view 2D images for the object of interest, generate a 3D silhouette model for the object of interest using the orthographic projection image and the perspective projection image, and output the 3D silhouette model and a guidance interface for the 3D silhouette model.

Abstract: Disclosed herein are a learning-based three-dimensional (3D) model creation apparatus and method. A method for operating a learning-based 3D model creation apparatus includes generating multi-view feature images using supervised learning, creating a three-dimensional (3D) mesh model using a point cloud corresponding to the multi-view feature images and a feature image representing internal shape information, generating a texture map by projecting the 3D mesh model into three viewpoint images that are input, and creating a 3D model using the texture map.

Abstract: Disclosed herein are a learning-based three-dimensional (3D) model creation apparatus and method. A method for operating a learning-based 3D model creation apparatus includes generating multi-view feature images using supervised learning, creating a three-dimensional (3D) mesh model using a point cloud corresponding to the multi-view feature images and a feature image representing internal shape information, generating a texture map by projecting the 3D mesh model into three viewpoint images that are input, and creating a 3D model using the texture map.

Abstract: A method and a system for generating a 3D image of a character through steps of: receiving an identifier of a template and a two-dimensional (2D) image of the character generated based on the template corresponding to a type of the character; acquiring template information of the template from a template library by using the identifier, and extracting an effective parameter for machine learning to be performed to generate the 3D image based on the template information and the 2D image; and generating the 3D image of the character by performing the machine learning based on the effective parameter and the template information are provided.

Abstract: Disclosed herein are an apparatus and method for generating a 3D model. The apparatus for generating a 3D model includes one or more processors, and an execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive two-dimensional (2D) original image layers for respective viewpoints, and generate pieces of 2D original image information for respective objects by performing original image alignment on the 2D original image layers for respective viewpoints for each predefined object type, generate 3D model layers for respective objects from the pieces of 2D original image information for respective objects using multiple learning models corresponding to the predefined object types, and generate a 3D model by synthesizing the 3D model layers for respective objects.

Abstract: Disclosed herein are an apparatus and method for guiding multi-view capture. The apparatus for guiding multi-view capture includes one or more processors and an execution memory for storing at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive a single-view two-dimensional (2D) image obtained by capturing an image of an object of interest through a camera, generate an orthographic projection image and a perspective projection image for the object of interest from the single-view 2D image using an image conversion parameter that is previously learned from multi-view 2D images for the object of interest, generate a 3D silhouette model for the object of interest using the orthographic projection image and the perspective projection image, and output the 3D silhouette model and a guidance interface for the 3D silhouette model.

Abstract: A camera auxiliary device for privacy protection and a privacy protection method using the camera auxiliary device. The camera auxiliary device for privacy protection includes a processor for splitting an input light beam that is reflected from a capturing target into a first input beam for detecting a privacy protection area and a second input beam to be transferred to a camera connected to a user terminal, detecting a privacy protection area in an image signal generated based on the first input beam, and converting the second input beam and then transferring a converted second input beam to the camera so that personal information included in the privacy protection area is not visually identified, and a memory for storing the image signal and the privacy protection area.

Abstract: Disclosed herein are a learning-based three-dimensional (3D) model creation apparatus and method. A method for operating a learning-based 3D model creation apparatus includes generating multi-view feature images using supervised learning, creating a three-dimensional (3D) mesh model using a point cloud corresponding to the multi-view feature images and a feature image representing internal shape information, generating a texture map by projecting the 3D mesh model into three viewpoint images that are input, and creating a 3D model using the texture map.

Abstract: A method and a system for generating a 3D image of a character through steps of: receiving an identifier of a template and a two-dimensional (2D) image of the character generated based on the template corresponding to a type of the character; acquiring template information of the template from a template library by using the identifier, and extracting an effective parameter for machine learning to be performed to generate the 3D image based on the template information and the 2D image; and generating the 3D image of the character by performing the machine learning based on the effective parameter and the template information are provided.

Abstract: Disclosed herein are an apparatus and method for generating a training data set for machine learning. The method for generating a training data set, performed by the apparatus for generating the training data set for machine learning, includes generating a 3D model for a deformed 3D character based on 3D data pertaining to the 3D character, generating a 2D image corresponding to the 3D model, and generating the training data set for machine learning, through which the 3D character is generated from the 2D image, using the 2D image and the 3D model.

Abstract: An apparatus for generating a 3D printing model using multiple textures includes a template model storage unit configured to store template models that are 3D models previously manufactured to be printable, a 3D model conversion unit configured to, upon receiving a 3D input model and a texture image, perform mesh deformation on the template model stored in the template model storage unit by using geometric information about the 3D input model, a texture processing unit configured to assign the texture image to the 3D model having being subjected to the mesh deformation, and a 3D printing model output unit configured to output a geometric model and a texture image of the template model which are finally calculated.

Abstract: Provided is a method and apparatus for reconstructing a three-dimensional (3D) face based on a stereo camera, the method including: acquiring n images of a target by controlling a plurality of stereo cameras in response to an image acquirement request, wherein n denotes a natural number; extracting n face regions from the n images, respectively; and reconstructing a viewpoint-based face image based on the n face regions.

Abstract: Disclosed herein is an apparatus and method for automatically creating a 3D personalized figure suitable for 3D printing by detecting a face area and features for respective regions from face data acquired by heterogeneous sensors and by optimizing global/local transformation. The 3D personalized figure creation apparatus acquires face data of a user corresponding to a reconstruction target; extracts feature points for respective regions from the face data, and reconstructs unique 3D models of the user's face, based on the extracted feature points; creates 3D figure models based on the unique 3D models and previously stored facial expression models and body/adornment models; and verifies whether each 3D figure model has a structure and a shape corresponding to actual 3D printing, corrects and edits the 3D figure model based on results of verification, and outputs a 3D figure model corresponding to 3D printing.

Abstract: An apparatus for generating a 3D printing model using multiple textures includes a template model storage unit configured to store template models that are 3D models previously manufactured to be printable, a 3D model conversion unit configured to, upon receiving a 3D input model and a texture image, perform mesh deformation on the template model stored in the template model storage unit by using geometric information about the 3D input model, a texture processing unit configured to assign the texture image to the 3D model having being subjected to the mesh deformation, and a 3D printing model output unit configured to output a geometric model and a texture image of the template model which are finally calculated.

Abstract: Provided is a three-dimensional (3D) object authoring apparatus for 3D printing. In the three-dimensional (3D) object authoring apparatus, a plurality of example objects having design components desired by a user may be created from a small number of previously created initial example objects by using a mesh deformation method in a nonlinear feature space. The 3D object desired by a user may be created by linearly composing example objects, having design components desired by the user, among a plurality of previously created example object.

Abstract: The present invention provides a local multi-resolution 3-D face-inherent model generation apparatus, including one or more 3-D facial model generation cameras for photographing a face of an object at various angles in order to obtain one or more 3-D face models, a 3-D face-inherent model generation unit for generating a 3-D face-inherent model by composing the one or more 3-D face models, a local photographing camera for photographing a local part of the face of the object, a control unit for controlling the position of the local photographing camera on the 3-D face-inherent model, and a local multi-resolution 3-D face-inherent model generation unit for generating a local multi-resolution face-inherent model by composing an image captured by the local photographing camera and the 3-D face-inherent model, a local multi-resolution 3-D face-inherent model generation using the local multi-resolution 3-D face-inherent model generation apparatus, and a skin management system.

Abstract: Disclosed herein are an apparatus and method for reconstructing a three-dimensional (3D) face based on multiple cameras. The apparatus includes a multi-image analysis unit, a texture image separation unit, a reconstruction image automatic synchronization unit, a 3D appearance reconstruction unit, and a texture processing unit. The multi-image analysis unit determines the resolution information of images received from a plurality of cameras, and determines whether the images have been synchronized with each other. The texture image separation unit separates a texture processing image by comparing the resolutions of the received images. The reconstruction image automatic synchronization unit synchronizes images that are determined to be asynchronous images by the multi-image analysis unit. The 3D appearance reconstruction unit computes the 3D coordinate values of the synchronized images, and reconstructs a 3D appearance image.

Abstract: Disclosed herein is an apparatus and method for automatically creating a 3D personalized figure suitable for 3D printing by detecting a face area and features for respective regions from face data acquired by heterogeneous sensors and by optimizing global/local transformation. The 3D personalized figure creation apparatus acquires face data of a user corresponding to a reconstruction target; extracts feature points for respective regions from the face data, and reconstructs unique 3D models of the user's face, based on the extracted feature points; creates 3D figure models based on the unique 3D models and previously stored facial expression models and body/adornment models; and verifies whether each 3D figure model has a structure and a shape corresponding to actual 3D printing, corrects and edits the 3D figure model based on results of verification, and outputs a 3D figure model corresponding to 3D printing.