MSTDF: Motion Style Transfer Towards High Visual Fidelity Based on Dynamic Fusion

Ziyun Qian, Dingkang Yang, Mingcheng Li, Zeyu Xiao, Lihua Zhang

Abstract: Emotion-guided motion style transfer is a novel research direction, enabling the efficient generation of motion in various emotional styles for use in films, games, and other domains. However, existing methods primarily rely on global feature statistics for motion style transfer, neglecting local semantic structure and resulting in the degradation of motion content structure. This letter proposes a novel Motion Style Transfer based on Dynamic Fusion (MSTDF) framework, which treats content and style motion as distinct signals and employs dynamic fusion for high-fidelity motion style transfer. Additionally, to address the challenge of traditional discriminators capturing subtle motion style features, we propose the Motion Dynamic Fusion (MDF) discriminator to capture the details and fine-grained style characteristics of motion sequences, assisting the generator in producing higher-fidelity stylized motion. Finally, extensive experiments on the Xia dataset demonstrate that our method surpasses state-of-the-art methods in qualitative and quantitative comparisons.