UMSD: High Realism Motion Style Transfer via Unified Mamba-based Diffusion

Motion style transfer is a significant research area in computer vision, enabling the rapid switching of stylistic variations for the same motion in virtual digital humans. This dramatically enhances the richness and realism of motions, making it widely applicable in multimedia contexts such as film, gaming, and the Metaverse. However, most existing methods employ a two-stream structure, which often overlooks the intrinsic relationships between content and style motions, resulting in information loss and misalignment. Additionally, these methods struggle to capture temporal dependencies in long-range motion sequences, resulting in less natural outputs. To address these limitations, we propose a Unified Motion Style Diffusion (UMSD) Framework that simultaneously extracts features from content and style motions, achieving comprehensive information interaction. We also introduce the Motion Style Mamba (MSM) denoiser, which, for the first time in motion style transfer, leverages Mamba's powerful sequence modelling capability to produce more temporally coherent stylized motion sequences. Furthermore, we design a diffusion-based content consistency loss and a style consistency loss to ensure that the framework preserves content motion while effectively learning style motion features. Extensive experiments demonstrate that our approach outperforms State-Of-The-Art (SOTA) methods qualitatively and quantitatively, achieving more realistic and coherent motion style transfer.