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About this paper
Jiayi He, Xu Wang, Shengeng Tang, Yaxiong Wang, Lechao Cheng, Dan Guo
The paper introduces a pose-centric framework for sign language video generation that decouples motion semantics from signer identity, enabling the synthesis of sign language for arbitrary signers with significantly reduced data requirements. This approach leverages a multimodal sign lexicon and a diffusion model for continuous motion transition, achieving state-of-the-art results in semantic accuracy and visual realism on the PHOENIX14T dataset.
Key insights
- Treating sign language motion as a universal, portable 'choreography layer' that can be decoupled from the visual appearance of any specific signer.
- Leveraging a multimodal, identity-agnostic lexicon of sign primitives drastically reduces data requirements and enhances generalization to novel sign combinations.
- Utilizing a diffusion model effectively generates seamless and natural transitions between discrete sign units, overcoming the temporal coherence issues of prior discrete unit approaches.
Summary by alphaXiv.
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