SMooDi: Stylized Motion Diffusion Model

The chapter 'SMooDi: Stylized Motion Diffusion Model' presents a groundbreaking approach to generating stylized human motions from textual content and style motion sequences. Traditional methods, such as motion capture and motion style transfer, are labor-intensive and limited in their ability to handle diverse motion styles. SMooDi overcomes these challenges by fine-tuning a pre-trained text-to-motion model to adapt to various motion styles. The key innovation lies in the introduction of a style modulation module, comprising a style adaptor and style guidance mechanisms. The style adaptor predicts residual features conditioned on style reference motion sequences, while the style guidance ensures the generated motion accurately reflects both content and style. Experiments on the HumanML3D and 100STYLE datasets demonstrate SMooDi's superior performance in generating stylized motions, outperforming baseline methods in content preservation, style reflection, and realism. This chapter is a must-read for professionals interested in advancing the field of AI-driven motion generation, offering a comprehensive solution to the complex problem of stylized motion creation.