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arXiv · 2508.14431

HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation

Bing Han·Yuhua Huang·Pan Gao
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Abstract

Monocular 3D human pose estimation (HPE) often encounters challenges such as depth ambiguity and occlusion during the 2D-to-3D lifting process. Additionally, traditional methods may overlook multi-scale skeleton features when utilizing skeleton structure information, which can negatively impact the accuracy of pose estimation. To address these challenges, this paper introduces a novel 3D pose estimation method, HyperDiff, which integrates diffusion models with HyperGCN. The diffusion model effectively captures data uncertainty, alleviating depth ambiguity and occlusion. Meanwhile, HyperGCN, serving as a denoiser, employs multi-granularity structures to accurately model high-order correlations between joints. This improves the model's denoising capability especially for complex poses. Experimental results demonstrate that HyperDiff achieves state-of-the-art performance on the Human3.6M and MPI-INF-3DHP datasets and can flexibly adapt to varying computational resources to balance performance and efficiency.

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Bing Han, Yuhua Huang, Pan Gao. 2025-08-20. HyperDiff: Hypergraph Guided Diffusion Model for 3D Human Pose Estimation. https://arxiv.org/abs/2508.14431

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