DiffPose: Toward More Reliable 3D Pose Estimation
This work addresses the problem of unreliable 3D pose estimation from single images for applications in computer vision and robotics, representing an incremental improvement with a novel method.
The authors tackled the challenge of monocular 3D human pose estimation, which suffers from ambiguity and occlusion, by proposing DiffPose, a framework that formulates it as a reverse diffusion process, and it significantly outperformed existing methods on benchmarks like Human3.6M and MPI-INF-3DHP.
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diffusion models have recently emerged as an effective tool for generating high-quality images from noise. Inspired by their capability, we explore a novel pose estimation framework (DiffPose) that formulates 3D pose estimation as a reverse diffusion process. We incorporate novel designs into our DiffPose to facilitate the diffusion process for 3D pose estimation: a pose-specific initialization of pose uncertainty distributions, a Gaussian Mixture Model-based forward diffusion process, and a context-conditioned reverse diffusion process. Our proposed DiffPose significantly outperforms existing methods on the widely used pose estimation benchmarks Human3.6M and MPI-INF-3DHP. Project page: https://gongjia0208.github.io/Diffpose/.