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arXiv · 2508.21363

Efficient Diffusion-Based 3D Human Pose Estimation with Hierarchical Temporal Pruning

Yuquan Bi·Hongsong Wang·Xinli Shi·Zhipeng Gui·Jie Gui·Yuan Yan Tang
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Abstract

Diffusion models have demonstrated strong capabilities in generating high-fidelity 3D human poses, yet their iterative nature and multi-hypothesis requirements incur substantial computational cost. In this paper, we propose an Efficient Diffusion-Based 3D Human Pose Estimation framework with a Hierarchical Temporal Pruning (HTP) strategy, which dynamically prunes redundant pose tokens across both frame and semantic levels while preserving critical motion dynamics. HTP operates in a staged, top-down manner: (1) Temporal Correlation-Enhanced Pruning (TCEP) identifies essential frames by analyzing inter-frame motion correlations through adaptive temporal graph construction; (2) Sparse-Focused Temporal MHSA (SFT MHSA) leverages the resulting frame-level sparsity to reduce attention computation, focusing on motion-relevant tokens; and (3) Mask-Guided Pose Token Pruner (MGPTP) performs fine-grained semantic pruning via clustering, retaining only the most informative pose tokens. Experiments on Human3.6M and MPI-INF-3DHP show that HTP reduces training MACs by 38.5\%, inference MACs by 56.8\%, and improves inference speed by an average of 81.1\% compared to prior diffusion-based methods, while achieving state-of-the-art performance.

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Yuquan Bi, Hongsong Wang, Xinli Shi, Zhipeng Gui, Jie Gui, Yuan Yan Tang. 2025-08-29. Efficient Diffusion-Based 3D Human Pose Estimation with Hierarchical Temporal Pruning. https://doi.org/10.1109/tcsvt.2026.3666928

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