  We introduce a novel approach for 3D whole-body pose estimation, addressing
the challenge of scale- and deformability- variance across body parts brought
by the challenge of extending the 17 major joints on the human body to
fine-grained keypoints on the face and hands. In addition to addressing the
challenge of exploiting motion in unevenly sampled data, we combine stable
diffusion to a hierarchical part representation which predicts the relative
locations of fine-grained keypoints within each part (e.g., face) with respect
to the part's local reference frame. On the H3WB dataset, our method greatly
outperforms the current state of the art, which fails to exploit the temporal
information. We also show considerable improvements compared to other
spatiotemporal 3D human-pose estimation approaches that fail to account for the
body part specificities. Code is available at
https://github.com/valeoai/PAFUSE.
