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About this paper
Haodong Zhang, ZhiKe Chen, Haocheng Xu, Lei Hao, Xiaofei Wu, Songcen Xu, Zhensong Zhang, Yue Wang and 1 more
Researchers from Zhejiang University and Huawei Noah’s Ark Lab introduce Semantics-aware Motion reTargeting (SMT), a novel method for preserving high-level motion semantics during character animation retargeting by integrating vision-language models as unsupervised semantic supervisors. The approach reduces mesh interpenetration to 3.50% and achieves an Image-Text Matching score of 0.680 for semantic consistency, outperforming previous methods.
Key insights
- Vision-language models can effectively provide unsupervised semantic supervision for motion retargeting when 3D motions are projected to 2D for analysis and guided with specific prompts.
- A two-stage training approach, separating skeleton-level pre-training from semantic and geometric fine-tuning, is critical for achieving both robust motion transfer and precise high-level semantic preservation.
- Utilizing latent semantic embeddings from the VLM's large language model encoder provides a more robust and denser representation of motion semantics compared to sparse text descriptions, improving consistency.
Summary by alphaXiv.
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