CoDePose: Multi-View 3D Human Pose Estimation via Coupled 2D-3D Denoising Diffusion

Recent multi-view 3D human pose estimation methods de-compose the problem into per-image 2D pose detection and 3D lift-ing stages, which can naturally scale to multi-frame input and achievestrong results by leveraging temporal context. However, this 2D-3D lift-ing paradigm introduces two levels of geometric inconsistency: (i) projec-tion inconsistency, i.e., the detected 2D keypoints may disagree with the2D projections of the inferred 3D pose, and (ii) cross-view inconsistency,i.e., 2D poses across views are mutually inconsistent because off-the-shelfdetectors operate independently per camera, so there may exist no sin-gle 3D pose whose projections match all detections. We observe that 2Ddetection errors closely follow a Gaussian distribution, meaning that de-tector outputs can be naturally viewed as noisy samples from a diffusionforward process. Based on this insight, we propose CoDePose, a coupled2D-3D denoising diffusion framework that jointly performs multi-viewdenoising of the detected 2D poses and estimates the underlying 3Dpose. As training proceeds, the generated joint distribution progressivelyapproaches the real data distribution, leading to increased projection andcross-view consistency and more reliable 3D estimation. Unlike existingdiffusion-based methods that condition on fixed 2D detections, CoDe-Pose refines the 2D observations during the reverse process, preventing2D detection errors and cross-view inconsistencies from being reinforcedthroughout the whole 3D pose generation. Experimental results on Hu-man3.6M show that CoDePose achieves 12.1 mm MPJPE, a 19.9% im-provement over the previous state-of-the-art method. Cross-dataset eval-uation on HumanEva and MPI-INF-3DHP further demonstrates stronggeneralization.