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Hand mesh reconstruction from the monocular image is a challenging task dueto its depth ambiguity and severe occlusion, there remains a non-unique mappingbetween the monocular image and hand mesh. To address this, we developDiffHand, the first diffusion-based framework that approaches hand meshreconstruction as a denoising diffusion process. Our one-stage pipelineutilizes noise to model the uncertainty distribution of the intermediate handmesh in a forward process. We reformulate the denoising diffusion process togradually refine noisy hand mesh and then select mesh with the highestprobability of being correct based on the image itself, rather than relying on2D joints extracted beforehand. To better model the connectivity of handvertices, we design a novel network module called the cross-modality decoder.Extensive experiments on the popular benchmarks demonstrate that our methodoutperforms the state-of-the-art hand mesh reconstruction approaches byachieving 5.8mm PA-MPJPE on the Freihand test set, 4.98mm PA-MPJPE on theDexYCB test set.

DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models