RAPTR: Radar-based 3D Pose Estimation using Transformer
Sorachi Kato, Ryoma Yataka, Pu Wang, Pedro Miraldo, Takuya Fujihashi, Petros Boufounos
Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) using only 3D BBox and 2D keypoint labels which are considerably easier and more scalable to collect.  Our RAPTR is characterized by a two-stage pose decoder architecture with a new pseudo-3D deformable attention to enhance (pose/joint) queries with multi-view radar features: a pose decoder estimates initial 3D poses with a 3D template loss designed to utilize the 3D BBox labels and mitigate depth ambiguities; and the refined joint decoder finalizes pose estimates with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by $34.2\%$ on HIBER and $76.9\%$ on MMVR.