Abstract: An electronic apparatus performs a method of representing a 3D shape that includes: dividing a 3D space enclosing the 3D shape into a plurality of 3D spaces with a hierarchical octree structure; generating local implicit functions, and each of the local implicit functions corresponds to a respective 3D space of the plurality of 3D spaces; and reconstructing a representation of the 3D shape from the local implicit functions with the hierarchical octree structure. In some embodiments, the 3D space is recursively subdivided into the child octants according to the surface occupancy and richness of the geometry of the 3D shape, and a respective local implicit function is generated corresponding to a geometry of a part of the surface.

Abstract: An electronic apparatus performs a method of constructing a facial position map from a two-dimensional (2D) facial image of a real-life person that includes: generating a coarse facial position map from the 2D facial image; predicting a first set of keypoints in the 2D facial image based on the coarse facial position map; identifying a second set of keypoints in the 2D facial image based on the user-provided keypoint annotations; and updating the coarse facial position map to get a final facial position map so as to reduce the differences between the first set of keypoints and the second set of key points in the 2D facial image. In some embodiments, a final set of keypoints and/or a three-dimensional (3D) facial model of the real-life person is formed from the final facial position map.

Abstract: An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Abstract: An electronic apparatus performs a method of generating a three-dimensional (3D) head deformation model that includes: receiving a two-dimensional (2D) facial image; identifying a first set of keypoints in the 2D facial image based on some artificial intelligence (AI) models; mapping the first set of keypoints to a second set of keypoints based on a set of user-provided keypoint annotations located on a plurality of vertices of a mesh of a 3D head template model; performing deformation to the mesh of the 3D head template model to obtain a deformed 3D head mesh model by reducing the differences between the first set of keypoints and the second set of keypoints; and applying a blendshape method to the deformed 3D head mesh model to obtain a personalized head model according to the 2D facial image.

Abstract: An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

Abstract: An electronic apparatus performs a method of representing a 3D shape that includes: dividing a 3D space enclosing the 3D shape into a plurality of 3D spaces with a hierarchical octree structure; generating local implicit functions, and each of the local implicit functions corresponds to a respective 3D space of the plurality of 3D spaces; and reconstructing a representation of the 3D shape from the local implicit functions with the hierarchical octree structure. In some embodiments, the 3D space is recursively subdivided into the child octants according to the surface occupancy and richness of the geometry of the 3D shape, and a respective local implicit function is generated corresponding to a geometry of a part of the surface.

Abstract: An electronic apparatus performs a method of extracting color from a two-dimensional (2D) facial image of a real-life person that includes: identifying a plurality of keypoints in the 2D facial image based on a keypoint prediction model; rotating the 2D facial image until the selected keypoints from the plurality of keypoints are aligned; locating a plurality of parts in the rotated 2D facial image, wherein each part is defined by a respective subset of the plurality of keypoints; extracting, from the pixel values of the 2D facial image, the average color for each of the plurality of the parts defined by a corresponding subset of keypoints; and generating a personalized three-dimensional (3D) model of the real-life person that mimics the respective facial feature color of the 2D facial image using the extracted colors of the plurality of the parts in the 2D facial image.

Abstract: An electronic apparatus performs a method of generating a three-dimensional (3D) head deformation model that includes: receiving a two-dimensional (2D) facial image; identifying a first set of keypoints in the 2D facial image based on some artificial intelligence (AI) models; mapping the first set of keypoints to a second set of keypoints based on a set of user-provided keypoint annotations located on a plurality of vertices of a mesh of a 3D head template model; performing deformation to the mesh of the 3D head template model to obtain a deformed 3D head mesh model by reducing the differences between the first set of keypoints and the second set of keypoints; and applying a blendshape method to the deformed 3D head mesh model to obtain a personalized head model according to the 2D facial image.

Abstract: An electronic apparatus performs a method of extracting color from a two-dimensional (2D) facial image of a real-life person that includes: identifying a plurality of keypoints in the 2D facial image based on a keypoint prediction model; rotating the 2D facial image until the selected keypoints from the plurality of keypoints are aligned; locating a plurality of parts in the rotated 2D facial image, wherein each part is defined by a respective subset of the plurality of keypoints; extracting, from the pixel values of the 2D facial image, the average color for each of the plurality of the parts defined by a corresponding subset of keypoints; and generating a personalized three-dimensional (3D) model of the real-life person that mimics the respective facial feature color of the 2D facial image using the extracted colors of the plurality of the parts in the 2D facial image.

Abstract: An electronic apparatus performs a method of constructing a facial position map from a two-dimensional (2D) facial image of a real-life person that includes: generating a coarse facial position map from the 2D facial image; predicting a first set of keypoints in the 2D facial image based on the coarse facial position map; identifying a second set of keypoints in the 2D facial image based on the user-provided keypoint annotations; and updating the coarse facial position map to get a final facial position map so as to reduce the differences between the first set of keypoints and the second set of key points in the 2D facial image. In some embodiments, a final set of keypoints and/or a three-dimensional (3D) facial model of the real-life person is formed from the final facial position map.

Abstract: An electronic apparatus performs a method of customizing a standard face of an avatar in a game using a two-dimensional (2D) facial image of a real-life person that includes: identifying a set of real-life keypoints in the 2D facial image; transforming the set of real-life keypoints into a set of game-style keypoints associated with the avatar in the game; generating a set of control parameters of the standard face of the avatar in the game by applying the set of game-style keypoints to a keypoint to parameter (K2P) neural network model; and deforming the standard face of the avatar in the game based on the set of control parameters, wherein the deformed face of the avatar has the facial features of the 2D facial image.