  Motion style transfer is a significant research direction in multimedia
applications. It enables the rapid switching of different styles of the same
motion for virtual digital humans, thus vastly increasing the diversity and
realism of movements. It is widely applied in multimedia scenarios such as
movies, games, and the Metaverse. However, most of the current work in this
field adopts the GAN, which may lead to instability and convergence issues,
making the final generated motion sequence somewhat chaotic and unable to
reflect a highly realistic and natural style. To address these problems, we
consider style motion as a condition and propose the Style Motion Conditioned
Diffusion (SMCD) framework for the first time, which can more comprehensively
learn the style features of motion. Moreover, we apply Mamba model for the
first time in the motion style transfer field, introducing the Motion Style
Mamba (MSM) module to handle longer motion sequences. Thirdly, aiming at the
SMCD framework, we propose Diffusion-based Content Consistency Loss and Content
Consistency Loss to assist the overall framework's training. Finally, we
conduct extensive experiments. The results reveal that our method surpasses
state-of-the-art methods in both qualitative and quantitative comparisons,
capable of generating more realistic motion sequences.
