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Human mesh recovery (HMR) provides rich human body information for variousreal-world applications. While image-based HMR methods have achieved impressiveresults, they often struggle to recover humans in dynamic scenarios, leading totemporal inconsistencies and non-smooth 3D motion predictions due to theabsence of human motion. In contrast, video-based approaches leverage temporalinformation to mitigate this issue. In this paper, we present DiffMesh, aninnovative motion-aware Diffusion-like framework for video-based HMR. DiffMeshestablishes a bridge between diffusion models and human motion, efficientlygenerating accurate and smooth output mesh sequences by incorporating humanmotion within the forward process and reverse process in the diffusion model.Extensive experiments are conducted on the widely used datasets (Human3.6M\cite{h36m_pami} and 3DPW \cite{pw3d2018}), which demonstrate the effectivenessand efficiency of our DiffMesh. Visual comparisons in real-world scenariosfurther highlight DiffMesh's suitability for practical applications.

DiffMesh: A Motion-aware Diffusion-like Framework for Human Mesh Recovery from Videos