SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation

Sobhan Asasi; Ozge Mercanoglu Sincan; Richard Bowden

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arXiv · 2606.28626

SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation

Sobhan Asasi·Ozge Mercanoglu Sincan·Richard Bowden

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Abstract

Sign language translation (SLT) remains challenging due to its high spatio-temporal complexity, long sequences, and the need to model multiple articulators without relying on gloss annotations. Existing approaches are typically tailored to individual datasets or languages and struggle to scale, while overlooking the relationships between sign languages that could inform more effective cross-lingual transfer. We present \textbf{SIGNET}, a framework that enables motion-level knowledge transfer for cross-language sign language translation. Our key insight is that, although sign languages differ in grammar and lexicon, pretrained models capture motion-level visual patterns that can be reused across datasets and languages. \textbf{SIGNET} integrates multiple pretrained sign language backbones through an attention-based, hand-prior aggregation mechanism that guides a gated fusion network in dynamically selecting the most relevant experts. Comprehensive experiments on four benchmarks (How2Sign, Phoenix14T, CSL-Daily, and MeineDGS) demonstrate state-of-the-art translation performance, and \textbf{SIGNET} also surpasses prior methods on WLASL for sign language recognition.

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BibTeX RIS

Sobhan Asasi, Ozge Mercanoglu Sincan, Richard Bowden. 2026-06-26. SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation. https://arxiv.org/abs/2606.28626

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