4 papers
OpenT2M: No-frill Motion Generation with Open-source,Large-scale, High-quality Data
Bin Cao, Sipeng Zheng, Hao Luo +3
Text-to-motion (T2M) generation aims to create realistic human movements from text descriptions, with promising applications in animation and robotics. Despite recent progress, cur…
Robust Motion Generation using Part-level Reliable Data from Videos
Boyuan Li, Sipeng Zheng, Bin Cao +2
Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames can…
Think-Then-React: Towards Unconstrained Human Action-to-Reaction Generation
Wenhui Tan, Boyuan Li, Chuhao Jin +3
Modeling human-like action-to-reaction generation has significant real-world applications, like human-robot interaction and games. Despite recent advancements in single-person moti…
Two-in-One: Unified Multi-Person Interactive Motion Generation by Latent Diffusion Transformer
Boyuan Li, Xihua Wang, Ruihua Song +1
Multi-person interactive motion generation, a critical yet under-explored domain in computer character animation, poses significant challenges such as intricate modeling of inter-h…