Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion
  Estimation
Paweł A. Pierzchlewicz Caio da Silva R. James Cotton Fabian H. Sinz
Single camera 3D pose estimation is an ill-defined problem due to inherent
ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose
estimation accounts for this uncertainty by providing multiple 3D poses
consistent with the 2D measurements. Current research has predominantly
concentrated on generating multiple hypotheses for single frame static pose
estimation. In this study we focus on the new task of multi-hypothesis motion
estimation. Motion estimation is not simply pose estimation applied to multiple
frames, which would ignore temporal correlation across frames. Instead, it
requires distributions which are capable of generating temporally consistent
samples, which is significantly more challenging. To this end, we introduce
Platypose, a framework that uses a diffusion model pretrained on 3D human
motion sequences for zero-shot 3D pose sequence estimation. Platypose
outperforms baseline methods on multiple hypotheses for motion estimation.
Additionally, Platypose also achieves state-of-the-art calibration and
competitive joint error when tested on static poses from Human3.6M,
MPI-INF-3DHP and 3DPW. Finally, because it is zero-shot, our method generalizes
flexibly to different settings such as multi-camera inference.