  3D human motion generation is crucial for creative industry. Recent advances
rely on generative models with domain knowledge for text-driven motion
generation, leading to substantial progress in capturing common motions.
However, the performance on more diverse motions remains unsatisfactory. In
this work, we propose ReMoDiffuse, a diffusion-model-based motion generation
framework that integrates a retrieval mechanism to refine the denoising
process. ReMoDiffuse enhances the generalizability and diversity of text-driven
motion generation with three key designs: 1) Hybrid Retrieval finds appropriate
references from the database in terms of both semantic and kinematic
similarities. 2) Semantic-Modulated Transformer selectively absorbs retrieval
knowledge, adapting to the difference between retrieved samples and the target
motion sequence. 3) Condition Mixture better utilizes the retrieval database
during inference, overcoming the scale sensitivity in classifier-free guidance.
Extensive experiments demonstrate that ReMoDiffuse outperforms state-of-the-art
methods by balancing both text-motion consistency and motion quality,
especially for more diverse motion generation.
