mlsanityData to 7 Oct 2026

RAPTR: Radar-based 3D Pose Estimation using Transformer

arXiv 2511.08387 ↗First posted 11 Nov 2025cs.CV cs.AI eess.SPcited by 2

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Abstract (arXiv)

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 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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 11 Nov 2025.

Builds on

34 of 51 references.

References · 34

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  11. 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
  12. 2019Distribution-Aware Coordinate Representation for Human Pose Estimationcited by 165
22 more
  1. 2021Human Pose Regression with Residual Log-likelihood Estimationcited by 131
  2. 20163D Human Pose Estimation = 2D Pose Estimation + Matchingcited by 112
  3. 2019Unsupervised 3D Pose Estimation with Geometric Self-Supervisioncited by 95
  4. 2023MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensingcited by 77
  5. 2017Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Imagecited by 73
  6. 2020Rethinking the Heatmap Regression for Bottom-up Human Pose Estimationcited by 69
  7. 2019mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNscited by 58
  8. 2022mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensorscited by 37
  9. 2019See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radarcited by 35
  10. 2022HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radarcited by 31
  11. 2023Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Reviewcited by 22
  12. 2024Diffusion Model is a Good Pose Estimator from 3D RF-Visioncited by 21
  13. 2023milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensingcited by 20
  14. 2021CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signalscited by 17
  15. 2024RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmarkcited by 16
  16. 2022RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signalscited by 13
  17. 2022Jointformer: Single-Frame Lifting Transformer with Error Prediction and Refinement for 3D Human Pose Estimationcited by 10
  18. 2024MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perceptioncited by 10
  19. 2023MMBaT: A Multi-task Framework for mmWave-based Human Body Reconstruction and Translation Predictioncited by 7
  20. 2024RETR: Multi-View Radar Detection Transformer for Indoor Perceptioncited by 6
  21. 2025mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstructioncited by 5
  22. 20244D Radar Semantic Segmentation of People in Field Conditions Using Temporal Multi-View Networkscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026mmSimPrior: Learning Simulation Priors for Data-Efficient and Generalizable Real-World Radar-based Human Motion Reconstructioncited by 0
  2. 2026Sound-based Multi-Person 3D Pose Estimationcited by 0