Reproduce “MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model” on OpenResearch

About this paper

Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, Ziwei Liu

MotionDiffuse introduces the first framework to apply Denoising Diffusion Probabilistic Models (DDPMs) for text-driven human motion generation. The model achieves state-of-the-art performance in synthesizing high-fidelity, diverse, and semantically relevant motions, while also offering novel fine-grained control over specific body parts and temporal action sequences.

Key insights

• Denoising Diffusion Probabilistic Models (DDPMs) are highly effective for modeling the complex, high-dimensional data distributions of human motion, enabling state-of-the-art fidelity and diversity.
• Leveraging pre-trained text encoders, particularly those initialized with CLIP, is crucial for robust semantic understanding of natural language prompts in text-driven motion generation.
• Efficient linear self-attention within a transformer architecture, combined with adaptive stylization blocks, allows for scalable processing of variable-length motion sequences and effective integration of textual and temporal conditions.

Summary by alphaXiv.