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About this paper
Zhaoyi An, Rei Kawakami
The Institute of Science Tokyo researchers developed TEACH ME SIGN (TEAM-Sign), a framework that leverages language-only Large Language Models (LLMs) to generate sign language from text. This method, using a VQ-VAE to discretize poses and a stepwise prompting strategy for LLMs, achieved approximately 17-19% improvement in pose accuracy (DTW-MJE) compared to prior methods on the Phoenix14T and How2Sign datasets.
Key insights
- Off-the-shelf, language-only LLMs possess implicit sign language knowledge and reasoning abilities that can be effectively extracted and utilized for sign language generation.
- A self-supervising mechanism, where LLMs generate internal auxiliary sequences, can overcome the limitations of external, human-annotated glosses and guide the learning process.
- Converting continuous sign language video information into discrete, text-compatible tokens via VQ-VAE enables direct processing and generation by text-focused LLMs.
Summary by alphaXiv.
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