61 citations · 576 across the 47 of their papers we have counts for
19 papers · 1 filter
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry
Lin Gao, Jia-Mu Sun, Kaichun Mo +3
3D indoor scenes are widely used in computer graphics, with applications ranging from interior design to gaming to virtual and augmented reality. They also contain rich information…
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
Yi-Hua Huang, Yue He, Yu-Jie Yuan +2
3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered fr…
NeRF-Editing: Geometry Editing of Neural Radiance Fields
Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai +3
Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable…
DeepFaceEditing: Deep Face Generation and Editing with Disentangled Geometry and Appearance Control
Shu-Yu Chen, Feng-Lin Liu, Yu-Kun Lai +4
Recent facial image synthesis methods have been mainly based on conditional generative models. Sketch-based conditions can effectively describe the geometry of faces, including the…
A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint
Yu-Jie Yuan, Yu-Kun Lai, Tong Wu +2
3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graph…
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
Jie Yang, Lin Gao, Qingyang Tan +3
Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions a…
TM-NET: Deep Generative Networks for Textured Meshes
Lin Gao, Tong Wu, Yu-Jie Yuan +3
We introduce TM-NET, a novel deep generative model for synthesizing textured meshes in a part-aware manner. Once trained, the network can generate novel textured meshes from scratc…
Geometry-guided Dense Perspective Network for Speech-Driven Facial Animation
Jingying Liu, Binyuan Hui, Kun Li +5
Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called…
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
Jie Yang, Kaichun Mo, Yu-Kun Lai +2
D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge…
Human Motion Transfer with 3D Constraints and Detail Enhancement
Yang-Tian Sun, Qian-Cheng Fu, Yue-Ren Jiang +4
We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of…
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry
Yi-Ling Qiao, Lin Gao, Shu-Zhi Liu +3
Reflectional symmetry is ubiquitous in nature. While extrinsic reflectional symmetry can be easily parametrized and detected, intrinsic symmetry is much harder due to the high solu…
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
Yi-Ling Qiao, Lin Gao, Jie Yang +3
3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is…
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
Lin Gao, Ling-Xiao Zhang, Hsien-Yu Meng +3
In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, ali…
Mesh Variational Autoencoders with Edge Contraction Pooling
Yu-Jie Yuan, Yu-Kun Lai, Jie Yang +2
3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based conv…
VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation
Hsien-Yu Meng, Lin Gao, YuKun Lai +1
We present a novel algorithm for point cloud segmentation. Our approach transforms unstructured point clouds into regular voxel grids, and further uses a kernel-based interpolated…
Content-Preserving Image Stitching with Regular Boundary Constraints
Yun Zhang, Yu-Kun Lai, Fang-Lue Zhang
This paper proposes an approach to content-preserving stitching of images with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with…
Sparse Data Driven Mesh Deformation
Lin Gao, Yu-Kun Lai, Jie Yang +3
Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can l…
Variational Autoencoders for Deforming 3D Mesh Models
Qingyang Tan, Lin Gao, Yu-Kun Lai +1
3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are fl…
Mesh-based Autoencoders for Localized Deformation Component Analysis
Qingyang Tan, Lin Gao, Yu-Kun Lai +2
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to…