  3D Human motion style transfer is a fundamental problem in computer graphic
and animation processing. Existing AdaIN- based methods necessitate datasets
with balanced style distribution and content/style labels to train the
clustered latent space. However, we may encounter a single unseen style example
in practical scenarios, but not in sufficient quantity to constitute a style
cluster for AdaIN-based methods. Therefore, in this paper, we propose a novel
two-stage framework for few-shot style transfer learning based on the diffusion
model. Specifically, in the first stage, we pre-train a diffusion-based
text-to-motion model as a generative prior so that it can cope with various
content motion inputs. In the second stage, based on the single style example,
we fine-tune the pre-trained diffusion model in a few-shot manner to make it
capable of style transfer. The key idea is regarding the reverse process of
diffusion as a motion-style translation process since the motion styles can be
viewed as special motion variations. During the fine-tuning for style transfer,
a simple yet effective semantic-guided style transfer loss coordinated with
style example reconstruction loss is introduced to supervise the style transfer
in CLIP semantic space. The qualitative and quantitative evaluations
demonstrate that our method can achieve state-of-the-art performance and has
practical applications.
