{"url":"/dataset/brace","name":"BRACE","full_name":"The Breakdancing Competition Dataset for Dance Motion Synthesis","description_markdown":"BRACE is a dataset for audio-conditioned dance motion synthesis challenging common assumptions for this task:\r\n\r\n- strong music-dance correlation\r\n- controlled motion data\r\n- simple poses and movements\r\n\r\nTo address these issues:\r\n\r\n- We focus on breakdancing which features acrobatic moves, tangled postures and weaker dance-music correlation. \r\n- We adopt a hybrid labelling pipeline leveraging estimation models as well as manual annotations to obtain good quality keypoint sequences at a reduced cost. \r\n- Our efforts produced the BRACE dataset, which contains over 3 hours and 30 minutes of densely annotated poses.\r\n- BRACE is also useful to fine-tune pose-estimation models thanks to its high quality keypoint annotations for complicated and uncommon poses.","description_withheld":null,"homepage":"https://github.com/dmoltisanti/brace/","introduced_date":"2022-07-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/brace-the-breakdancing-competition-dataset","title":"BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis","first_author":"Davide Moltisanti","url":null},"license":{"name":"S-Lab License 1.0","url":"https://github.com/dmoltisanti/brace/#license"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"Actions","url":"/datasets/modality/actions"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Motion Synthesis","url":"/task/motion-synthesis","datasets_with_task":"/datasets/task/motion-synthesis"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BRACE"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/motion-synthesis-on-brace","task":"Motion Synthesis","dataset_variant":"BRACE","rows":3,"metrics":["Frechet Inception Distance","Beat alignment score","Beat DTW cost","Footwork average","Powermove average","Toprock average"],"first_row_in_archive_order":{"model":"Dance Revolution","paper":"/paper/dance-revolution-long-sequence-dance","metrics":{"Beat DTW cost":"11.88","Beat alignment score":"0.264","Footwork average":"51.6","Frechet Inception Distance":"0.5158","Powermove average":"37.72","Toprock average":"10.59"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/pose-estimation-on-brace","task":"Pose Estimation","dataset_variant":"BRACE","rows":2,"metrics":["Average Precision","Average Recall"],"first_row_in_archive_order":{"model":"HRNet fine-tuned on BRACE","paper":"/paper/deep-high-resolution-representation-learning","metrics":{"Average Precision":"0.357","Average Recall":"0.445"},"code_links":[{"title":"open-mmlab/mmdetection","url":"https://github.com/open-mmlab/mmdetection"},{"title":"PaddlePaddle/PaddleDetection","url":"https://github.com/PaddlePaddle/PaddleDetection"},{"title":"open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose"},{"title":"leoxiaobin/deep-high-resolution-net.pytorch","url":"https://github.com/leoxiaobin/deep-high-resolution-net.pytorch"},{"title":"HRNet/HRNet-Semantic-Segmentation","url":"https://github.com/HRNet/HRNet-Semantic-Segmentation"},{"title":"osmr/imgclsmob","url":"https://github.com/osmr/imgclsmob"},{"title":"Microsoft/human-pose-estimation.pytorch","url":"https://github.com/Microsoft/human-pose-estimation.pytorch"},{"title":"HRNet/HRNet-Facial-Landmark-Detection","url":"https://github.com/HRNet/HRNet-Facial-Landmark-Detection"},{"title":"HRNet/HRNet-Image-Classification","url":"https://github.com/HRNet/HRNet-Image-Classification"},{"title":"HRNet/HRNet-Object-Detection","url":"https://github.com/HRNet/HRNet-Object-Detection"},{"title":"mindspore-lab/mindone","url":"https://github.com/mindspore-lab/mindone"},{"title":"leeyegy/SimDR","url":"https://github.com/leeyegy/SimDR"},{"title":"leeyegy/simcc","url":"https://github.com/leeyegy/simcc"},{"title":"mks0601/PoseFix_RELEASE","url":"https://github.com/mks0601/PoseFix_RELEASE"},{"title":"HRNet/HRNet-Human-Pose-Estimation","url":"https://github.com/HRNet/HRNet-Human-Pose-Estimation"},{"title":"strivebo/image_segmentation_dl","url":"https://github.com/strivebo/image_segmentation_dl"},{"title":"HRNet/HRNet-MaskRCNN-Benchmark","url":"https://github.com/HRNet/HRNet-MaskRCNN-Benchmark"},{"title":"CASIA-IVA-Lab/ISP-reID","url":"https://github.com/CASIA-IVA-Lab/ISP-reID"},{"title":"NVlabs/PAMTRI","url":"https://github.com/NVlabs/PAMTRI"},{"title":"k-miran/hear","url":"https://github.com/k-miran/hear"},{"title":"Vill-Lab/2022-TIP-HCGA","url":"https://github.com/Vill-Lab/2022-TIP-HCGA"},{"title":"d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification","url":"https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification"},{"title":"chuanqichen/deepcoaching","url":"https://github.com/chuanqichen/deepcoaching"},{"title":"Mary-xl/HRnet_Kaggle_iNat2019_FGVC","url":"https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC"},{"title":"v1viswan/Domain_adaptation_in_HRNet","url":"https://github.com/v1viswan/Domain_adaptation_in_HRNet"},{"title":"ken724049/action-recognition","url":"https://github.com/ken724049/action-recognition"},{"title":"NU-LL/lighttrack-","url":"https://github.com/NU-LL/lighttrack-"},{"title":"thoughtmachines/Human-Pose-Estimation-using-HRNets","url":"https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets"},{"title":"ducongju/HRNet","url":"https://github.com/ducongju/HRNet"},{"title":"thomasslloyd/FitSpatial","url":"https://github.com/thomasslloyd/FitSpatial"},{"title":"laowang666888/HRNET","url":"https://github.com/laowang666888/HRNET"},{"title":"baoshengyu/deep-high-resolution-net.pytorch","url":"https://github.com/baoshengyu/deep-high-resolution-net.pytorch"},{"title":"sdll/hrnet-pose-estimation","url":"https://github.com/sdll/hrnet-pose-estimation"},{"title":"gox-ai/hrnet-pose-api","url":"https://github.com/gox-ai/hrnet-pose-api"},{"title":"anshky/HR-NET","url":"https://github.com/anshky/HR-NET"},{"title":"wsjzha/deep-high-resolution-net.pytorch","url":"https://github.com/wsjzha/deep-high-resolution-net.pytorch"},{"title":"visionNoob/hrnet_pytorch","url":"https://github.com/visionNoob/hrnet_pytorch"},{"title":"abhi1kumar/hrnet_pose_single_gpu","url":"https://github.com/abhi1kumar/hrnet_pose_single_gpu"},{"title":"goutern/PoseEstimation","url":"https://github.com/goutern/PoseEstimation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learn-to-dance-with-aist-music-conditioned-3d","title":"AI Choreographer: Music Conditioned 3D Dance Generation with AIST++","date":"2021-01-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dance-revolution-long-sequence-dance","title":"Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning","date":"2020-06-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dancing-to-music","title":"Dancing to Music","date":"2019-11-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-high-resolution-representation-learning","title":"Deep High-Resolution Representation Learning for Human Pose Estimation","date":"2019-02-25","rows_on_this_dataset":2,"code_links":39,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":25,"samples_ran":8,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":25,"samples_ran":8,"samples_unverified":17,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}