SkyPose: Skeleton consistency guided diffusion model for 3D human pose estimation

Although diffusion models have demonstrated promising potential in 3D human pose estimation, existing approaches commonly adopt fixed noise variance scheduling strategies. This design overlooks the skeletal consistency constraints that should be maintained between 2D observations and 3D predictions during iterative denoising. Consequently, the generated 3D poses often exhibit geometric inconsistencies with the input 2D poses, thereby degrading the overall reconstruction quality. To address this limitation, we propose SkyPose: Skeleton Consistency Guided Diffusion Model for 3D Human Pose Estimation. The proposed method enforces skeletal consistency by reprojecting the previously generated 3D pose onto the 2D plane, decomposing it into skeletal direction and length, and comparing it with the input 2D skeletal structure to construct a dynamic skeletal consistency metric. This metric adaptively adjusts the noise variance σt during DDIM iterations, explicitly preserving skeletal structural coherence throughout the denoising process and enhancing the geometric fidelity of the generated 3D poses. Furthermore, we redesign the denoising network architecture to integrate skeletal structure features with joint coordinate features at the local joint, body-part, and full-body levels, thereby enabling joint–skeleton collaborative denoising under multi-granularity geometric constraints. Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that our approach achieves superior accuracy compared with existing methods. The code is open-sourced at here.