122 citations · 314 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
MotionBERT: A Unified Perspective on Learning Human Motion Representations
Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu +3
We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifica…
Rhythmic Gesticulator: Rhythm-Aware Co-Speech Gesture Synthesis with Hierarchical Neural Embeddings
Tenglong Ao, Qingzhe Gao, Yuke Lou +2
Automatic synthesis of realistic co-speech gestures is an increasingly important yet challenging task in artificial embodied agent creation. Previous systems mainly focus on genera…
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…
Learning to Use Chopsticks in Diverse Gripping Styles
Zeshi Yang, KangKang Yin, Libin Liu
Learning dexterous manipulation skills is a long-standing challenge in computer graphics and robotics, especially when the task involves complex and delicate interactions between t…