mlsanityData to 14 Oct 2026

RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation

arXiv 2601.00625 ↗First posted 2 Jan 2026cs.CVcited by 0

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Abstract (arXiv)

We propose a real-time 3D human pose estimation and motion analysis method termed RePose for rehabilitation training. It is capable of real-time monitoring and evaluation of patients'motion during rehabilitation, providing immediate feedback and guidance to assist patients in executing rehabilitation exercises correctly. Firstly, we introduce a unified pipeline for end-to-end real-time human pose estimation and motion analysis using RGB video input from multiple cameras which can be applied to the field of rehabilitation training. The pipeline can help to monitor and correct patients'actions, thus aiding them in regaining muscle strength and motor functions. Secondly, we propose a fast tracking method for medical rehabilitation scenarios with multiple-person interference, which requires less than 1ms for tracking for a single frame. Additionally, we modify SmoothNet for real-time posture estimation, effectively reducing pose estimation errors and restoring the patient's true motion state, making it visually smoother. Finally, we use Unity platform for real-time monitoring and evaluation of patients' motion during rehabilitation, and to display the muscle stress conditions to assist patients with their rehabilitation training.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 2 Jan 2026.

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25 of 50 references.

References · 25

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2017Mask R-CNNcited by 7,277
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  4. 2015Fast R-CNNcited by 2,933
  5. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  6. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
  7. 2018Object Detection with Deep Learning: A Reviewcited by 528
  8. 20183D human pose estimation in video with temporal convolutions and semi-supervised trainingcited by 403
  9. 2016Convolutional Pose Machinescited by 325
  10. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  11. 2020Deep Learning-Based Human Pose Estimation: A Surveycited by 207
  12. 2019Learnable Triangulation of Human Posecited by 161
13 more
  1. 2021MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimationcited by 151
  2. 2017DSSD : Deconvolutional Single Shot Detectorcited by 131
  3. 2017Compositional Human Pose Regressioncited by 124
  4. 2023PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimationcited by 92
  5. 2021Direct Multi-view Multi-person 3D Pose Estimationcited by 64
  6. 2021SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videoscited by 64
  7. 2017DSOD: Learning Deeply Supervised Object Detectors from Scratchcited by 41
  8. 2020AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the Wildcited by 40
  9. 2022Faster VoxelPose: Real-time 3D Human Pose Estimation by Orthographic Projectioncited by 39
  10. 2019A Deep Learning Framework for Assessing Physical Rehabilitation Exercisescited by 26
  11. 2020Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severitycited by 18
  12. 2025ViC-Bench: Benchmarking Visual-Interleaved Chain-of-Thought Capability in MLLMs with Free-Style Intermediate State Representationscited by 12
  13. 2025Scalable Audio-Visual Masked Autoencoders for Efficient Affective Video Facial Analysiscited by 5

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