[Submitted on 9 Sep 2026]
Title:MHE-Former: Multi-Hypothesis Transformers via Entropy Maximization for 3D Mesh Recovery
View a PDF of the paper titled MHE-Former: Multi-Hypothesis Transformers via Entropy Maximization for 3D Mesh Recovery, by Boshu Jia and 10 other authors
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Abstract:Monocular 3D hand and body mesh recovery often suffers from severe occlusion and ambiguity. Traditional deterministic methods typically regress a single optimal solution, leading to overconfident predictions. In this paper, we introduce an exploration--exploitation paradigm for ambiguous mesh recovery with multi-hypothesis learning and selection. Specifically, during exploration, based on our probabilistic formulation and entropy maximization, we propose a novel multi-hypothesis method referred to as MHE-Former. It is a Transformer-based multi-hypothesis framework, ensuring high training efficiency and label friendliness while generating plausible and diverse hypotheses. During exploitation, we propose Hypothesis Selection, a context-aware process for multiple predictions. Especially leveraging VLM's powerful visual understanding and reasoning capabilities, it allows users to choose the most plausible and desired estimate with additional evidence and natural language intent. Extensive experiments demonstrate that our framework achieves state-of-the-art performance in accuracy and diversity across multiple datasets. The user preference study further shows the practicality of our hypothesis selection process.
Comments: 14 pages, 11 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.10743 [cs.CV]
(or arXiv:2609.10743v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.10743
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Boshu Jia [view email] [v1] Wed, 9 Sep 2026 18:39:03 UTC (5,537 KB)
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