Unsupervised 3D Human Pose Estimation via Conditional Multi-view Ancestral Sampling

ArXi:2605.15583v1 Announce Type: new We propose a method of estimating a 3D human pose from a single view without 3D supervision. The key to our method is to leverage the 2D diffusion priors of motion diffusion models (MDMs) pre-trained on large 2D human pose datasets. Specifically, we extend multi-view ancestral sampling of diffusion models to the task of 2D-3D lifting of human pose.