mlsanityData to 14 Oct 2026

PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing

arXiv 2503.00068 ↗First posted 27 Feb 2025cs.CV cs.ETcited by 3

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Abstract (arXiv)

Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PI-HMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 46 of its 65 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

46 of 65 references.

References · 46

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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  12. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
34 more
  1. 2023Humans in 4D: Reconstructing and Tracking Humans with Transformerscited by 350
  2. 2021PARE: Part Attention Regressor for 3D Human Body Estimationcited by 337
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  4. 2019Resolving 3D Human Pose Ambiguities with 3D Scene Constraintscited by 295
  5. 2021PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loopcited by 250
  6. 2022CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimationcited by 238
  7. 2023WHAM: Reconstructing World-grounded Humans with Accurate 3D Motioncited by 206
  8. 2022Generating Holistic 3D Human Motion from Speechcited by 180
  9. 2020Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Videocited by 163
  10. 2021SPEC: Seeing People in the Wild with an Estimated Cameracited by 135
  11. 2023One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformercited by 130
  12. 2019Exploiting temporal context for 3D human pose estimation in the wildcited by 110
  13. 2023EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wildcited by 97
  14. 2024TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representationcited by 95
  15. 2022InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interactioncited by 93
  16. 2021On Self-Contact and Human Posecited by 89
  17. 2022Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Videocited by 60
  18. 2023Global-to-Local Modeling for Video-based 3D Human Pose and Shape Estimationcited by 47
  19. 2023ReFit: Recurrent Fitting Network for 3D Human Recoverycited by 47
  20. 2023Generative Proxemics: A Prior for 3D Social Interaction from Imagescited by 43
  21. 2020Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Datacited by 34
  22. 2023DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusioncited by 31
  23. 2023Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Videocited by 25
  24. 2022SUPR: A Sparse Unified Part-Based Human Representationcited by 21
  25. 2024Stratified Avatar Generation from Sparse Observationscited by 21
  26. 2023Decaf: Monocular Deformation Capture for Face and Hand Interactionscited by 18
  27. 2019Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimationcited by 17
  28. 2024MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensorscited by 17
  29. 2020Multimodal In-bed Pose and Shape Estimation under the Blanketscited by 15
  30. 20183D Human Pose Estimation on a Configurable Bed from a Pressure Imagecited by 13
  31. 2017In-Bed Pose Estimation: Deep Learning with Shallow Datasetcited by 11
  32. 2024Pose Priors from Language Modelscited by 6
  33. 2024BodyMAP -- Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bedcited by 5
  34. 2023Human Pose Estimation from Ambiguous Pressure Recordings with Spatio-temporal Masked Transformerscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025On the Use of Hierarchical Vision Foundation Models for Low-Cost Human Mesh Recovery and Pose Estimationcited by 0
  2. 2025PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recoverycited by 0
  3. 2026PressureMesh: 3D Human Mesh Estimation from Multi-Device Pressure Imagescited by 0