AMASS: Archive of Motion Capture As Surface Shapes
Open accessAbstract
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 [Loper et al., 2015], 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 11000 motions, and will be publicly available to the research community.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Braun, W. J., & Murdoch, D. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993456.011 · Cited by 14,099 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,713 · see citing works
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734 · see citing works
Self-citationSCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489 · see citing works
Dueck, G., & Scheuer, T. (1990) · Journal of Computational Physics · doi:10.1016/0021-9991(90)90201-b · Cited by 1,250 · see citing works
Human movement analysis using stereophotogrammetry
Leardini, A., Chiari, L., Croce, U. D., & Cappozzo, A. (2005) · Gait & Posture · doi:10.1016/j.gaitpost.2004.05.002 · Cited by 1,239 · see citing works
Learning a model of facial shape and expression from 4D scans
Li, T., Bolkart, T., Black, M. J., Li, H., & Romero, J. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130813 · Cited by 1,226 · see citing works
Self-citationSigal, L., Bălan, A. O., & Black, M. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0273-6 · Cited by 1,202 · see citing works
Self-citationRomero (2017) · doi:10.1145/3130800.3130883 · Cited by 1,056 · see citing works
Decomposing biological motion: A framework for analysis and synthesis of human gait patterns
Troje, N. F. (2002) · Journal of Vision · doi:10.1167/2.5.2 · Cited by 1,018 · see citing works
Self-citationLearning from Synthetic Humans
Varol, G., Romero, J., Martín, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.492 · Cited by 915 · see citing works
Self-citationThe space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913 · see citing works
Human movement analysis using stereophotogrammetry
Cappozzo, A., Croce, U. D., Leardini, A., & Chiari, L. (2004) · Gait & Posture · doi:10.1016/j.gaitpost.2004.01.010 · Cited by 840 · see citing works
The space of human body shapes
Allen, B., Curless, B., & Popović, Z. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882311 · Cited by 725 · see citing works
Three-dimensional kinematics of the human knee during walking
Lafortune, M. A., Cavanagh, P. R., Sommer, H. J., & Kalenak, A. (1992) · Journal of Biomechanics · doi:10.1016/0021-9290(92)90254-x · Cited by 688 · see citing works
A deep learning framework for character motion synthesis and editing
Holden, D., Saito, J., & Komura, T. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925975 · Cited by 656 · see citing works
Pose-conditioned joint angle limits for 3D human pose reconstruction
Akhter, I., & Black, M. J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298751 · Cited by 451 · see citing works
Self-citationDocumentation Mocap Database HDM05
Müller, M., Röder, T., Clausen, M. V., Eberhardt, B., Krüger, B., & Weber⋆, A. (2007) · Cited by 378 · see citing works
Loper, M., Mahmood, N., & Black, M. J. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661273 · Cited by 344 · see citing works
Self-citationPons‐Moll, G., Romero, J., Mahmood, N., & Black, M. J. (2015) · ACM Transactions on Graphics · doi:10.1145/2766993 · Cited by 323 · see citing works
Self-citationTotal Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors
Trumble, M., Gilbert, A., Malleson, C., Hilton, A., & Collomosse, J. P. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.14 · Cited by 301 · see citing works
Taylor, J., Bordeaux, L., Cashman, T. J., Corish, B., Keskin, C., Sharp, T., Soto, E., Sweeney, D., Valentin, J., Luff, B., et al. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925965 · Cited by 295 · see citing works
Assessment of the kinematic variability among 12 motion analysis laboratories
Gorton, G. E., Hebert, D. A., & Gannotti, M. E. (2008) · Gait & Posture · doi:10.1016/j.gaitpost.2008.10.060 · Cited by 288 · see citing works
Studies of human locomotion: past, present and future
Andriacchi, T. P., & Alexander, E. J. (2000) · Journal of Biomechanics · doi:10.1016/s0021-9290(00)00061-0 · Cited by 255 · see citing works
Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database
Torre, F. D. L., Hodgins, J. K., Bargteil, A. W., et al. (2018) · KiltHub Repository · doi:10.1184/r1/6555020.v1 · Cited by 236 · see citing works
The KIT whole-body human motion database
Mandery, C., Terlemez, Ö., Do, M., Vahrenkamp, N., & Asfour, T. (2015) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251476 · Cited by 210 · see citing works
Coregistration: Simultaneous Alignment and Modeling of Articulated 3D Shape
Hirshberg, D. A., Loper, M., Rachlin, E., & Black, M. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33783-3_18 · Cited by 173 · see citing works
Self-citationRobust solving of optical motion capture data by denoising
Holden, D. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201302 · Cited by 119 · see citing works
Online optical marker-based hand tracking with deep labels
Han, S., Liu, B., Wang, R., et al. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201399 · Cited by 119 · see citing works
Efficient and robust annotation of motion capture data
Müller, M., Baak, A., & Seidel, H. (2009) · ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA) · doi:10.1145/1599470.1599473 · Cited by 109 · see citing works
Allen, B. L., Curless, B., Popović, Z., & Hertzmann, A. (2006) · doi:10.5555/1218064.1218084 · Cited by 94 · see citing works
Online generative model personalization for hand tracking
Tkach, A., Tagliasacchi, A., Remelli, E., Pauly, M., & Fitzgibbon, A. (2017) · ACM Transactions on Graphics · doi:10.1145/3130800.3130830 · Cited by 91 · see citing works
Hoyet, L., Ryall, K., McDonnell, R., et al. (2012) · ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) · doi:10.1145/2159616.2159630 · Cited by 53 · see citing works
Reduced marker layouts for optical motion capture of hands
Schröder, M., Maycock, J., & Botsch, M. (2015) · Motion in Games · doi:10.1145/2822013.2822026 · Cited by 28 · see citing works
Fully automatic optical motion tracking using an inverse kinematics approach
Maycock, J., Röhlig, T., Schröder, M., et al. (2015) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2015.7363590 · Cited by 23 · see citing works
Auto-labelling of Markers in Optical Motion Capture by Permutation Learning
Ghorbani, S., Etemad, A., & Troje, N. F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22514-8_14 · Cited by 19 · see citing works
Self-citationRobust online motion capture labeling of finger markers
Alexanderson, S., O’Sullivan, C., & Beskow, J. (2016) · doi:10.1145/2994258.2994264 · Cited by 16 · see citing works
von Laßberg, C., Rapp, W., Mohler, B. J., et al. (2013) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2013.07.006 · Cited by 12 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- M. Loper, N. Mahmood, and M. J. Black. MoSh: Motion and shape capture from sparse markers. ACM Transactions on Graphics (TOG), 2014. 2, 3, 4, 7, 8, 12
- G. Pons-Moll, J. Romero, N. Mahmood, and M. J. Black. Dyna: A Model of Dynamic Human Shape in Motion. ACM Transactions on Graphics, (Proc. SIGGRAPH), 2015. 3, 4,
Cited by
Sorted by citations
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Li, R., Yang, S., Ross, D. A., & Kanazawa, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01315 · Cited by 553 · see citing works
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2022) · arXiv · doi:10.48550/arxiv.2208.15001 · Cited by 437 · see citing works
Generating Human Motion from Textual Descriptions with Discrete Representations
Zhang, J., Zhang, Y., Cun, X., Zhang, Y., Zhao, H., Lu, H., Shen, X., & Ying, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01415 · Cited by 328 · see citing works
BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion
Black, M. J., Patel, P., Tesch, J., & Yang, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00843 · Cited by 148 · see citing works
Self-citationAcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wild
Joska, D., Clark, L., Muramatsu, N., Jericevich, R., Nicolls, F., Mathis, A., Mathis, M. W., & Patel, A. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561338 · Cited by 66 · see citing works
Kwon, H., Abowd, G. D., & Plötz, T. (2021) · Sensors · doi:10.3390/s21248337 · Cited by 30 · see citing works
Habitat 3.0: A Co-Habitat for Humans, Avatars, and Robots
Puig, X., Undersander, E., Szot, A., Cote, M. D., Yang, T.-Y., Partsey, R., Desai, R., Clegg, A. W., Hlavac, M., Min, S. Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.13724 · Cited by 15 · see citing works
“For animating the forward motion, we use a walking motion clip from the AMASS dataset (Mahmood et al., 2019), trim it to contain a single walking cycle (after both the left and right foot have moved) and play it cyclically until reaching the next waypoint (Fig.”
MethodKwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Ploetz, T. (2020) · arXiv · doi:10.48550/arxiv.2006.05675 · Cited by 14 · see citing works
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3 · see citing works
Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning
Luo, Z., Tessler, C., Lin, T., Yuan, Y., He, T., Xiao, W., Guo, Y., Chechik, G., Kitani, K., Fan, L., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.12278
- Authors
- Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M.
- Affiliations
- Max Planck Institute for Intelligent Systems (DE)
- York University (CA)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 5441-5450
- OpenAlex
- W2928521819
- Cited by
- 1,323
- References
- 39
- Citations by year
- 2026: 170 · 2025: 287 · 2024: 327 · 2023: 221 · 2022: 138 · 2021: 126 · 2020: 48 · 2019: 6
- FWCI
- 122.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- human motion capture, AMASS, mineral oil saturated hydrocarbons, 3D human mesh, SMPL, hand movement, human motion modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
