1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CV2025
FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance
Dian Shao, Mingfei Shi, Shengda Xu +3
Despite significant advances in video generation, synthesizing physically plausible human actions remains a persistent challenge, particularly in modeling fine-grained semantics an…
cs.CV2025★ 1 cited
SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization
Yongle Huang, Haodong Chen, Zhenbang Xu +3
Human action understanding is crucial for the advancement of multimodal systems. While recent developments, driven by powerful large language models (LLMs), aim to be general enoug…