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arXiv · 2511.00270

POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation

Abhinav Joshi·Vaibhav Sharma·Sanjeet Singh·Ashutosh Modi
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Abstract

Sign language translation remains a challenging task due to the scarcity of large-scale, sentence-aligned datasets. Prior arts have focused on various feature extraction and architectural changes to support neural machine translation for sign languages. We propose POSESTITCH-SLT, a novel pre-training scheme that is inspired by linguistic-templates-based sentence generation technique. With translation comparison on two sign language datasets, How2Sign and iSign, we show that a simple transformer-based encoder-decoder architecture outperforms the prior art when considering template-generated sentence pairs in training. We achieve BLEU-4 score improvements from 1.97 to 4.56 on How2Sign and from 0.55 to 3.43 on iSign, surpassing prior state-of-the-art methods for pose-based gloss-free translation. The results demonstrate the effectiveness of template-driven synthetic supervision in low-resource sign language settings.

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Abhinav Joshi, Vaibhav Sharma, Sanjeet Singh, Ashutosh Modi. 2025-10-31. POSESTITCH-SLT: Linguistically Inspired Pose-Stitching for End-to-End Sign Language Translation. https://arxiv.org/abs/2511.00270

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