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arXiv Computer Vision By Boshu Jia, Rongyu Chen, Linlin Yang, Zihao Liu, Yingjie Chen, Zhongqun Zhang, Zhulin Tao, Shaohui Lin, Xiaoyu Wu, Libiao Jin, Baochang Zhang, Angela Yao

MHE-Former: Multi-Hypothesis Transformers via Entropy Maximization for 3D Mesh Recovery

Read the original on arXiv Computer Vision →

MHE-Former introduces a Transformer-based multi-hypothesis framework for monocular 3D hand and body mesh recovery, addressing occlusion and ambiguity by maximizing entropy during exploration. The method generates diverse, plausible mesh hypotheses efficiently and includes a context-aware hypothesis selection process that leverages visual language models and natural language intent. Experiments show state‑of‑the‑art accuracy and diversity, and a user preference study confirms the practicality of the selection mechanism.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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