82 citations · 86 across the 3 of their papers we have counts for
4 papers
ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
Heyuan Yao, Zhenhua Song, Baoquan Chen +1
In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). Our framework can lea…
Neural3Points: Learning to Generate Physically Realistic Full-body Motion for Virtual Reality Users
Yongjing Ye, Libin Liu, Lei Hu +1
Animating an avatar that reflects a user's action in the VR world enables natural interactions with the virtual environment. It has the potential to allow remote users to communica…
Unsupervised Co-part Segmentation through Assembly
Qingzhe Gao, Bin Wang, Libin Liu +1
Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For th…
Learning Skeletal Articulations with Neural Blend Shapes
Peizhuo Li, Kfir Aberman, Rana Hanocka +3
Animating a newly designed character using motion capture (mocap) data is a long standing problem in computer animation. A key consideration is the skeletal structure that should c…