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In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art method for editable dance generation that is capable of creating realistic, physically-plausible dances while remaining faithful to the input music. EDGE uses a transformer-based diffusion model paired with Jukebox, a strong music feature extractor, and confers powerful editing capabilities well-suited to dance, including joint-wise conditioning, and in-betweening. We introduce a new metric for physical plausibility, and evaluate dance quality generated by our method extensively through (1) multiple quantitative metrics on physical plausibility, beat alignment, and diversity benchmarks, and more importantly, (2) a large-scale user study, demonstrating a significant improvement over previous state-of-the-art methods. Qualitative samples from our model can be found at our website.","url_abs":"https://arxiv.org/abs/2211.10658v2","url_pdf":"https://arxiv.org/pdf/2211.10658v2.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":"edge-editable-dance-generation-from-music","repo_url":"https://github.com/Stanford-TML/EDGE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"jukebox","method_name":"Jukebox"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"vq-vae","method_name":"VQ-VAE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-synthesis-on-aist","task":"Motion Synthesis","dataset":"AIST++","model":"EDGE (w=1)","rank_in_archive_order":11,"of":12,"metrics":{"Beat alignment score":"0.27"},"uses_additional_data":false},{"leaderboard":"/sota/motion-synthesis-on-aist","task":"Motion Synthesis","dataset":"AIST++","model":"EDGE (w=2)","rank_in_archive_order":12,"of":12,"metrics":{"Beat alignment score":"0.26"},"uses_additional_data":false},{"leaderboard":"/sota/motion-synthesis-on-finedance","task":"Motion Synthesis","dataset":"FineDance","model":"EDGE","rank_in_archive_order":6,"of":8,"metrics":{"BAS":"0.2116","fid_k":"94.34"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2211.10658","atlas_url":"https://app.syntology.ai/?focus=2211.10658","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.10658"}},"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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