Multi-Hypothesis 3D Hand Mesh Recovering from a Single Blurry Image

Recovery of 3D hand mesh from blurry hand images is challenging due to the ambiguity. Most existing works attempt to solve this issue by exploiting physical and temporal constraints. However, those works ignore the fact that multiple feasible solutions exist. In this paper, we propose a two-stage Multi-Hypothesis Hand Mesh Recovery network, consisting of a generation and selection model. In the first stage, the generation model explicitly extracts the temporal information with an unfolder. Then, a multi-hypothesis Transformer generates multiple diverse hypotheses with a lightweight hypothesis embedding set. In the second stage, the selection model selects a subset of good-quality hypotheses. We additionally combine the classifying and ranking loss to better align with the target of the selection model. Extensive experiments show that the proposed method produces much more accurate results on blurry images. Source code is available at https://github.com/RandSF/Multi_Hypothesis_BlurHandNet.