DiFusion: Flexible Stylized Motion Generation Using Digest-and-Fusion Scheme

To address the issue of style expression in existing text-driven human motion synthesis methods, we propose DiFusion, a framework for diversely stylized motion generation. It offers flexible control of content through texts and style via multiple modalities, i.e., textual labels or motion sequences. Our approach employs a dual-condition motion latent diffusion model, enabling independent control of content and style through flexible input modalities. To tackle the issue of imbalanced complexity between the text-motion and style-motion datasets, we propose the Digest-and-Fusion training scheme, which digests domain-specific knowledge from both datasets and then adaptively fuses them into a compatible manner. Comprehensive evaluations demonstrate the effectiveness of our method and its superiority over existing approaches in terms of content alignment, style expressiveness, realism, and diversity. Additionally, our approach can be extended to practical applications, such as motion style interpolation.