DRPose: A Diffusion-Based Pose Refinement Framework for 3D Human Pose Estimation

Recently, two-stage 3D human pose estimation using monocular cameras has gained significant attention. However, the inherent uncertainty in the upscaling process from 2D to 3D often compromises the accuracy of deterministic methods. To address this, we propose a novel diffusion-based refinement framework (DRPose) which models the uncertainty during the upscaling process by introducing stochastic noise to the initially predicted 3D poses. This approach facilitates the generation of more realistic predictions through iterative refinement with multiple noise samples, ultimately producing multi-hypothesis predictions that better align with ground truth. Our framework incorporates two key components: a Graph Convolution Transformer module (SGCT), which integrates scaling and displacement adjustments based on conditional information with a joint temporal-spatial feature separation mechanism, and a Pose Refinement Module (PRM), which balances the initial and refined poses. This design allows DRPose to effectively refine pose estimation for both individual frames and sequential data. Furthermore, our framework establishes new benchmarks for performance in bothframe2frameandseq2framescenarios. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Human3.6M and MPI-INF-3DHP datasets. Notably, when applied to the current state-of-the-art single-frame 3D pose extractor, our multi-hypothesis optimization achieves an 18.8% reduction in Mean Per Joint Position Error (MPJPE) and a 16.9% reduction in Procrustes MPJPE (P-MPJPE). Code is available at https://github.com/KHB1698/DRPose.