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

2025-08-20

AI Summary

Monocular 3D human pose estimation suffers from depth ambiguity and occlusion-induced accuracy degradation, while existing methods struggle to adequately model multi-scale, high-order structural dependencies among joints. This paper proposes a novel framework integrating hypergraph convolution with diffusion modeling: HyperGCN serves as the diffusion denoiser, explicitly capturing multi-granularity, high-order joint correlations via hypergraphs to enhance robustness against occlusion and depth uncertainty; simultaneously, the diffusion process probabilistically models the inherent ambiguity in the 2D-to-3D mapping. Evaluated on Human3.6M and MPI-INF-3DHP, our method achieves state-of-the-art performance—outperforming prior works in accuracy while maintaining computational scalability and enabling efficient deployment under diverse resource constraints.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addresses depth ambiguity and occlusion in 3D pose estimation
• Overlooks multi-scale skeleton features in traditional methods
• Models high-order correlations between human body joints

Innovation

• Hypergraph guided diffusion model
• HyperGCN denoiser captures joint correlations
• Multi-granularity structures handle complex poses