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

TL;DR

HyperDiff combines diffusion models with HyperGCN to improve 3D human pose estimation from monocular images, effectively addressing depth ambiguity and occlusion while capturing multi-scale skeleton features for enhanced accuracy.

Contribution

The paper introduces HyperDiff, a novel approach integrating diffusion models with HyperGCN to better model uncertainties and high-order joint correlations in 3D pose estimation.

Findings

Achieves state-of-the-art results on Human3.6M and MPI-INF-3DHP datasets.

Effectively handles depth ambiguity and occlusion issues.

Balances performance and efficiency across different computational settings.

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.