3 papers
cs.CV2026
IAM: Identity-Aware Human Motion and Shape Joint Generation
Wenqi Jia, Zekun Li, Abhay Mittal +6
Recent advances in text-driven human motion generation enable models to synthesize realistic motion sequences from natural language descriptions. However, most existing approaches…
cs.CV2026
UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
Xiaoyan Cong, Zekun Li, Zhiyang Dou +9
Large-scale foundation models (LFMs) have recently made impressive progress in text-to-motion generation by learning strong generative priors from massive 3D human motion datasets…
cs.CV2026
EgoReAct: Egocentric Video-Driven 3D Human Reaction Generation
Libo Zhang, Zekun Li, Tianyu Li +10
Humans exhibit adaptive, context-sensitive responses to egocentric visual input. However, faithfully modeling such reactions from egocentric video remains challenging due to the du…