MAFD: Fine-Grained Motion Style Transfer with Adaptive Signal Fusion

Abstract

Motion style transfer allows for the swift switching of different styles within the same motion for virtual avatars, offering significant efficiency gains and enhanced motion diversity compared to traditional motion capture methods. However, many existing methods struggle with controlling fine details in complex motions, leading to models that capture only coarse-grained style characteristics. To overcome this limitation, we introduce the Motion Adaptive Fusion Diffusion (MAFD) framework, which leverages adaptive signal fusion to highlight essential style-defining features while minimizing redundant information. Moreover, current diffusion-based denoisers often fail to effectively capture the temporal relationships in motion sequences, producing rigid and fragmented stylized motions. Drawing inspiration from the Mamba model, we propose the Style Mamba Denoiser (SMD), which adopts a selection mechanism to preserve long-range dependencies and maintain temporal coherence. Extensive experiments show that our approach outperforms state-of-the-art methods in both qualitative and quantitative evaluations, achieving more refined and coherent stylized motions.