{"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/ude-a-unified-driving-engine-for-human-motion","title":"UDE: A Unified Driving Engine for Human Motion Generation","arxiv_id":"2211.16016","date":"2022-11-29","proceeding":"CVPR 2023 1","authors":["Zixiang Zhou","Baoyuan Wang"],"abstract":"Generating controllable and editable human motion sequences is a key challenge in 3D Avatar generation. It has been labor-intensive to generate and animate human motion for a long time until learning-based approaches have been developed and applied recently. However, these approaches are still task-specific or modality-specific\\cite {ahuja2019language2pose}\\cite{ghosh2021synthesis}\\cite{ferreira2021learning}\\cite{li2021ai}. In this paper, we propose ``UDE\", the first unified driving engine that enables generating human motion sequences from natural language or audio sequences (see Fig.~\\ref{fig:teaser}). Specifically, UDE consists of the following key components: 1) a motion quantization module based on VQVAE that represents continuous motion sequence as discrete latent code\\cite{van2017neural}, 2) a modality-agnostic transformer encoder\\cite{vaswani2017attention} that learns to map modality-aware driving signals to a joint space, and 3) a unified token transformer (GPT-like\\cite{radford2019language}) network to predict the quantized latent code index in an auto-regressive manner. 4) a diffusion motion decoder that takes as input the motion tokens and decodes them into motion sequences with high diversity. We evaluate our method on HumanML3D\\cite{Guo_2022_CVPR} and AIST++\\cite{li2021learn} benchmarks, and the experiment results demonstrate our method achieves state-of-the-art performance. Project website: \\url{https://github.com/zixiangzhou916/UDE/","url_abs":"https://arxiv.org/abs/2211.16016v1","url_pdf":"https://arxiv.org/pdf/2211.16016v1.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":"ude-a-unified-driving-engine-for-human-motion","repo_url":"https://github.com/zixiangzhou916/ude","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"motion-generation","task_name":"Motion Generation"},{"task_slug":"motion-synthesis","task_name":"Motion Synthesis"},{"task_slug":"quantization","task_name":"Quantization"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/motion-synthesis-on-aist","task":"Motion Synthesis","dataset":"AIST++","model":"UDE","rank_in_archive_order":2,"of":12,"metrics":{"Beat alignment score":"0.2311","FID":"17.25"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.16016","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}