In recent years, the emergence of generative models has spurred development
of human motion generation, among which the generation of stylized human motion
has consistently been a focal point of research. The conventional approach for
stylized human motion generation involves transferring the style from given
style examples to new motions. Despite decades of research in human motion
style transfer, it still faces three main challenges: 1) difficulties in
decoupling the motion content and style; 2) generalization to unseen motion
style. 3) requirements of dedicated motion style dataset; To address these
issues, we propose an on-the-fly human motion style transfer learning method
based on the diffusion model, which can learn a style transfer model in a few
minutes of fine-tuning to transfer an unseen style to diverse content motions.
The key idea of our method is to consider the denoising process of the
diffusion model as a motion translation process that learns the difference
between the style-neutral motion pair, thereby avoiding the challenge of style
and content decoupling. Specifically, given an unseen style example, we first
generate the corresponding neutral motion through the proposed Style-Neutral
Motion Pair Generation module. We then add noise to the generated neutral
motion and denoise it to be close to the style example to fine-tune the style
transfer diffusion model. We only need one style example and a text-to-motion
dataset with predominantly neutral motion (e.g. HumanML3D). The qualitative and
quantitative evaluations demonstrate that our method can achieve
state-of-the-art performance and has practical applications.

我们提出了一种基于扩散模型的即时人体运动风格转移学习方法，通过几分钟的微调学习，可以将未知的风格转移到不同的内容运动中，且仅需要一个风格示例和以中立运动为主的文本到运动数据集（例如 HumanML3D）。