{"url":"/dataset/amass","name":"AMASS","full_name":null,"description_markdown":"AMASS is a large database of human motion unifying different optical marker-based motion capture datasets by representing them within a common framework and parameterization. AMASS is readily useful for animation, visualization, and generating training data for deep learning.","description_withheld":null,"homepage":"https://amass.is.tue.mpg.de/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/amass-archive-of-motion-capture-as-surface","title":"AMASS: Archive of Motion Capture as Surface Shapes","first_author":"Naureen Mahmood","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://amass.is.tue.mpg.de/license"},"modalities":[{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"3D Human Pose Estimation","url":"/task/3d-human-pose-estimation","datasets_with_task":"/datasets/task/3d-human-pose-estimation"},{"name":"Human Pose Forecasting","url":"/task/human-pose-forecasting","datasets_with_task":"/datasets/task/human-pose-forecasting"}],"languages":[],"variants":["AMASS"],"data_loaders":[],"num_papers_in_archive":366,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/human-pose-forecasting-on-amass","task":"Human Pose Forecasting","dataset_variant":"AMASS","rows":11,"metrics":["Average MPJPE (mm) 1000 msec","FDE@1000ms (mm)","FDE@560ms (mm)","FDE@720ms (mm)","FDE@880ms (mm)","ADE","FDE","APD","APDE"],"first_row_in_archive_order":{"model":"STS-GCN","paper":"/paper/space-time-separable-graph-convolutional-1","metrics":{"Average MPJPE (mm) 1000 msec":"45.5"},"code_links":[{"title":"fraluca/stsgcn","url":"https://github.com/fraluca/stsgcn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gcnext-towards-the-unity-of-graph","title":"GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction","date":"2023-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/anypose-anytime-3d-human-pose-forecasting-via","title":"AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations","date":"2023-09-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transfusion-a-practical-and-effective","title":"TransFusion: A Practical and Effective Transformer-based Diffusion Model for 3D Human Motion Prediction","date":"2023-07-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-accurate-human-motion-prediction-via","title":"Towards Accurate Human Motion Prediction via Iterative Refinement","date":"2023-05-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/belfusion-latent-diffusion-for-behavior","title":"BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction","date":"2022-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-generic-diffusion-based-approach-for-3d","title":"A generic diffusion-based approach for 3D human pose prediction in the wild","date":"2022-10-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/back-to-mlp-a-simple-baseline-for-human","title":"Back to MLP: A Simple Baseline for Human Motion Prediction","date":"2022-07-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/space-time-separable-graph-convolutional-1","title":"Space-Time-Separable Graph Convolutional Network for Pose Forecasting","date":"2021-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generating-smooth-pose-sequences-for-diverse","title":"Generating Smooth Pose Sequences for Diverse Human Motion Prediction","date":"2021-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dlow-diversifying-latent-flows-for-diverse","title":"DLow: Diversifying Latent Flows for Diverse Human Motion Prediction","date":"2020-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-pose-knows-video-forecasting-by","title":"The Pose Knows: Video Forecasting by Generating Pose Futures","date":"2017-04-28","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":20,"samples_ran":5,"samples_unverified":15,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":2,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}