{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/amass-archive-of-motion-capture-as-surface","title":"AMASS: Archive of Motion Capture as Surface Shapes","arxiv_id":"1904.03278","date":"2019-04-05","proceeding":"ICCV 2019 10","authors":["Naureen Mahmood","Nima Ghorbani","Nikolaus F. Troje","Gerard Pons-Moll","Michael J. Black"],"abstract":"Large datasets are the cornerstone of recent advances in computer vision\nusing deep learning. In contrast, existing human motion capture (mocap)\ndatasets are small and the motions limited, hampering progress on learning\nmodels of human motion. While there are many different datasets available, they\neach use a different parameterization of the body, making it difficult to\nintegrate them into a single meta dataset. To address this, we introduce AMASS,\na large and varied database of human motion that unifies 15 different optical\nmarker-based mocap datasets by representing them within a common framework and\nparameterization. We achieve this using a new method, MoSh++, that converts\nmocap data into realistic 3D human meshes represented by a rigged body model;\nhere we use SMPL [doi:10.1145/2816795.2818013], which is widely used and\nprovides a standard skeletal representation as well as a fully rigged surface\nmesh. The method works for arbitrary marker sets, while recovering soft-tissue\ndynamics and realistic hand motion. We evaluate MoSh++ and tune its\nhyperparameters using a new dataset of 4D body scans that are jointly recorded\nwith marker-based mocap. The consistent representation of AMASS makes it\nreadily useful for animation, visualization, and generating training data for\ndeep learning. Our dataset is significantly richer than previous human motion\ncollections, having more than 40 hours of motion data, spanning over 300\nsubjects, more than 11,000 motions, and will be publicly available to the\nresearch community.","url_abs":"http://arxiv.org/abs/1904.03278v1","url_pdf":"http://arxiv.org/pdf/1904.03278v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"amass-archive-of-motion-capture-as-surface","repo_url":"https://github.com/nghorbani/moshpp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"amass-archive-of-motion-capture-as-surface","repo_url":"https://github.com/AemikaChow/DATASOURCE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"amass-archive-of-motion-capture-as-surface","repo_url":"https://github.com/mohsenzand/motionflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"amass-archive-of-motion-capture-as-surface","repo_url":"https://github.com/nghorbani/amass","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"amass","name":"AMASS","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.03278","atlas_url":"https://app.syntology.ai/?focus=1904.03278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03278"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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