{"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/dancing-to-music","title":"Dancing to Music","arxiv_id":"1911.02001","date":"2019-11-05","proceeding":"NeurIPS 2019 12","authors":["Hsin-Ying Lee","Xiaodong Yang","Ming-Yu Liu","Ting-Chun Wang","Yu-Ding Lu","Ming-Hsuan Yang","Jan Kautz"],"abstract":"Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music.","url_abs":"https://arxiv.org/abs/1911.02001v1","url_pdf":"https://arxiv.org/pdf/1911.02001v1.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":"dancing-to-music","repo_url":"https://github.com/NVlabs/Dance2Music","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"dancing-to-music","repo_url":"https://github.com/nvlabs/dancing2music","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-synthesis-on-brace","task":"Motion Synthesis","dataset":"BRACE","model":"Dancing 2 Music","rank_in_archive_order":3,"of":3,"metrics":{"Beat DTW cost":"11.60","Beat alignment score":"0.129","Footwork average":"50.09","Frechet Inception Distance":"0.5884","Powermove average":"33.87","Toprock average":"16.04"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.02001","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}