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) under weak supervision, 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 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 a joint decoder refines the initial poses with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by 34.3% on HIBER and 76.9% on MMVR. Our implementation is available at https://github.com/merlresearch/radar-pose-transformer.
@article{arxiv.2511.08387,
title = {RAPTR: Radar-based 3D Pose Estimation using Transformer},
author = {Sorachi Kato and Ryoma Yataka and Pu Perry Wang and Pedro Miraldo and Takuya Fujihashi and Petros Boufounos},
journal= {arXiv preprint arXiv:2511.08387},
year = {2025}
}