RAPTR: Radar-based 3D Pose Estimation using Transformer

• URL: http://arxiv.org/abs/2511.08387v1
• Date: Wed, 12 Nov 2025 01:56:55 GMT
• Title: RAPTR: Radar-based 3D Pose Estimation using Transformer
• Authors: Sorachi Kato, Ryoma Yataka, Pu Perry Wang, Pedro Miraldo, Takuya Fujihashi, Petros Boufounos,
• Abstract summary: Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels.<n>We propose textbfRAPTR (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint labels.
• Score: 24.646708425495472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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