  Stylized motion breathes life into characters. However, the fixed skeleton
structure and style representation hinder existing data-driven motion synthesis
methods from generating stylized motion for various characters. In this work,
we propose a generative motion stylization pipeline, named MotionS, for
synthesizing diverse and stylized motion on cross-structure characters using
cross-modality style prompts. Our key insight is to embed motion style into a
cross-modality latent space and perceive the cross-structure skeleton
topologies, allowing for motion stylization within a canonical motion space.
Specifically, the large-scale Contrastive-Language-Image-Pre-training (CLIP)
model is leveraged to construct the cross-modality latent space, enabling
flexible style representation within it. Additionally, two topology-encoded
tokens are learned to capture the canonical and specific skeleton topologies,
facilitating cross-structure topology shifting. Subsequently, the
topology-shifted stylization diffusion is designed to generate motion content
for the particular skeleton and stylize it in the shifted canonical motion
space using multi-modality style descriptions. Through an extensive set of
examples, we demonstrate the flexibility and generalizability of our pipeline
across various characters and style descriptions. Qualitative and quantitative
comparisons show the superiority of our pipeline over state-of-the-arts,
consistently delivering high-quality stylized motion across a broad spectrum of
skeletal structures.
