mlsanityData to 14 Oct 2026

Improving Human Motion Plausibility with Body Momentum

arXiv 2509.09496 ↗First posted 11 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

Many studies decompose human motion into local motion in a frame attached to the root joint and global motion of the root joint in the world frame, treating them separately. However, these two components are not independent. Global movement arises from interactions with the environment, which are, in turn, driven by changes in the body configuration. Motion models often fail to precisely capture this physical coupling between local and global dynamics, while deriving global trajectories from joint torques and external forces is computationally expensive and complex. To address these challenges, we propose using whole-body linear and angular momentum as a constraint to link local motion with global movement. Since momentum reflects the aggregate effect of joint-level dynamics on the body's movement through space, it provides a physically grounded way to relate local joint behavior to global displacement. Building on this insight, we introduce a new loss term that enforces consistency between the generated momentum profiles and those observed in ground-truth data. Incorporating our loss reduces foot sliding and jitter, improves balance, and preserves the accuracy of the recovered motion. Code and data are available at the project page https://hlinhn.github.io/momentum_bmvc.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 49 of its 71 references that are in the map. First posted on arXiv 11 Sep 2025.

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49 of 71 references.

References · 49

  1. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,236
  2. 2022Human Motion Diffusion Modelcited by 1,080
  3. 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
  4. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 588
  5. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 575
  6. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 501
  7. 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 494
  8. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 492
  9. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 368
  10. 2023Humans in 4D: Reconstructing and Tracking Humans with Transformerscited by 350
  11. 2016The KIT Motion-Language Datasetcited by 338
  12. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 324
37 more
  1. 2018Learning 3D Human Dynamics from Videocited by 316
  2. 2022CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimationcited by 238
  3. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 227
  4. 2023WHAM: Reconstructing World-grounded Humans with Accurate 3D Motioncited by 206
  5. 2020DLow: Diversifying Latent Flows for Diverse Human Motion Predictioncited by 185
  6. 2022Capturing and Inferring Dense Full-Body Human-Scene Contactcited by 180
  7. 2017HP-GAN: Probabilistic 3D human motion prediction via GANcited by 176
  8. 2021GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Camerascited by 153
  9. 2023Decoupling Human and Camera Motion from Videos in the Wildcited by 142
  10. 2018Human Motion Prediction via Spatio-Temporal Inpaintingcited by 114
  11. 2021SimPoE: Simulated Character Control for 3D Human Pose Estimationcited by 114
  12. 2024TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videoscited by 106
  13. 20233D Human Pose Estimation via Intuitive Physicscited by 103
  14. 2021Learning Motion Priors for 4D Human Body Capture in 3D Scenescited by 98
  15. 2023Optimizing Diffusion Noise Can Serve As Universal Motion Priorscited by 98
  16. 2023EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoningcited by 97
  17. 2023EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wildcited by 97
  18. 2020Contact and Human Dynamics from Monocular Videocited by 95
  19. 2023TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environmentscited by 91
  20. 2021Neural Monocular 3D Human Motion Capture with Physical Awarenesscited by 71
  21. 2022NeMF: Neural Motion Fields for Kinematic Animationcited by 68
  22. 2021Physics-based Human Motion Estimation and Synthesis from Videoscited by 66
  23. 2020Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesiscited by 64
  24. 2019MoGlow: Probabilistic and controllable motion synthesis using normalising flowscited by 47
  25. 2021Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformerscited by 46
  26. 2022D&D: Learning Human Dynamics from Dynamic Cameracited by 41
  27. 2022Differentiable Dynamics for Articulated 3d Human Motion Reconstructioncited by 39
  28. 2022Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Videocited by 37
  29. 2023PACE: Human and Camera Motion Estimation from in-the-wild Videoscited by 34
  30. 2021Task-Generic Hierarchical Human Motion Prior using VAEscited by 33
  31. 2022Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capturecited by 31
  32. 2024PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videoscited by 28
  33. 2022BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Trackingcited by 24
  34. 2024HUMOS: Human Motion Model Conditioned on Body Shapecited by 20
  35. 2022PoseBERT: A Generic Transformer Module for Temporal 3D Human Modelingcited by 15
  36. 2024AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scalecited by 11
  37. 2023GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstructioncited by 4

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