MotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 46; no. 6; pp. 4115 - 4128
• Main Authors: Zhang, Mingyuan, Cai, Zhongang, Pan, Liang, Hong, Fangzhou, Guo, Xinying, Yang, Lei, Liu, Ziwei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Body parts; Computer Graphics; Conditional motion generation; Decoding; diffusion model; Human motion; Humans; Image Processing, Computer-Assisted - methods; Mapping; Modelling; Motion; motion synthesis; Movement - physiology; Noise reduction; Pipelines; Probabilistic logic; Qualitative analysis; Sequences; Synthesis; Task analysis; text-driven generation; Training; Transformers
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2024.3355414

Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions conditioned on natural languages. However, it remains challenging to achieve diverse and fine-grained motion generation with various text inputs. To address this problem, we propose MotionDiffuse , one of the first diffusion model-based text-driven motion generation frameworks, which demonstrates several desired properties over existing methods. 1) Probabilistic Mapping . Instead of a deterministic language-motion mapping, MotionDiffuse generates motions through a series of denoising steps in which variations are injected. 2) Realistic Synthesis . MotionDiffuse excels at modeling complicated data distribution and generating vivid motion sequences. 3) Multi-Level Manipulation . MotionDiffuse responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts. Our experiments show MotionDiffuse outperforms existing SoTA methods by convincing margins on text-driven motion generation and action-conditioned motion generation. A qualitative analysis further demonstrates MotionDiffuse's controllability for comprehensive motion generation.