mlsanityData to 7 Oct 2026

AMASS: Archive of Motion Capture as Surface Shapes

arXiv 1904.03278 ↗First posted 5 Apr 2019cs.CV cs.GRcited by 1,208

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Cited by 143 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset. To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization. We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model; here we use SMPL [doi:10.1145/2816795.2818013], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh. The method works for arbitrary marker sets, while recovering soft-tissue dynamics and realistic hand motion. We evaluate MoSh++ and tune its hyperparameters using a new dataset of 4D body scans that are jointly recorded with marker-based mocap. The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning. Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11,000 motions, and will be publicly available to the research community.

Key facts. Cited by 1,208 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2019.

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Citing papers per year: 2021: 78; 2022: 121; 2023: 218; 2024: 259; 2025: 289; 2026: 243.

1,208 papers.

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  1. 2022Human Motion Diffusion Modelcited by 1,062
  2. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
  3. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  4. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  5. 2023MotionGPT: Human Motion as a Foreign Languagecited by 521
  6. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  7. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 486
  8. 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 479
  9. 2023Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectivescited by 477
  10. 2022MotionCLIP: Exposing Human Motion Generation to CLIP Spacecited by 406
  11. 2023MoMask: Generative Masked Modeling of 3D Human Motionscited by 388
  12. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
38 more
  1. 2023Human Motion Diffusion as a Generative Priorcited by 354
  2. 2023Humans in 4D: Reconstructing and Tracking Humans with Transformerscited by 344
  3. 2021HuMoR: 3D Human Motion Model for Robust Pose Estimationcited by 319
  4. 2022ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulationcited by 306
  5. 2023ReMoDiffuse: Retrieval-Augmented Motion Diffusion Modelcited by 282
  6. 2023PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Trackingcited by 274
  7. 2021BABEL: Bodies, Action and Behavior with English Labelscited by 261
  8. 2022CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimationcited by 237
  9. 2023OmniControl: Control Any Joint at Any Time for Human Motion Generationcited by 233
  10. 2023BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motioncited by 229
  11. 2021SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapescited by 221
  12. 2023Guided Motion Diffusion for Controllable Human Motion Synthesiscited by 221
  13. 2023Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Datasetcited by 220
  14. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
  15. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
  16. 2022FLAME: Free-form Language-based Motion Synthesis & Editingcited by 217
  17. 2023Object Motion Guided Human Motion Synthesiscited by 206
  18. 2021Stochastic Scene-Aware Motion Predictioncited by 203
  19. 2023WHAM: Reconstructing World-grounded Humans with Accurate 3D Motioncited by 200
  20. 2023Perpetual Humanoid Control for Real-time Simulated Avatarscited by 198
  21. 20233DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splattingcited by 195
  22. 2023TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesiscited by 194
  23. 2022MotionBERT: A Unified Perspective on Learning Human Motion Representationscited by 188
  24. 2022Capturing and Inferring Dense Full-Body Human-Scene Contactcited by 176
  25. 2022TEACH: Temporal Action Composition for 3D Humanscited by 173
  26. 2022Fake it till you make it: Learning transferable representations from synthetic ImageNet clonescited by 171
  27. 2024OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learningcited by 167
  28. 2022Towards Metrical Reconstruction of Human Facescited by 165
  29. 2024MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Modelcited by 164
  30. 2024HumanPlus: Humanoid Shadowing and Imitation from Humanscited by 164
  31. 2022HUMANISE: Language-conditioned Human Motion Generation in 3D Scenescited by 156
  32. 2023HUGS: Human Gaussian Splatscited by 154
  33. 2021Probabilistic Modeling for Human Mesh Recoverycited by 152
  34. 2021GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Camerascited by 151
  35. 2023MotionGPT: Finetuned LLMs Are General-Purpose Motion Generatorscited by 145
  36. 2022ARAH: Animatable Volume Rendering of Articulated Human SDFscited by 139
  37. 2022InstantAvatar: Learning Avatars from Monocular Video in 60 Secondscited by 138
  38. 2023Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusioncited by 137

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