Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment

• URL: http://arxiv.org/abs/2512.08040v1
• Date: Mon, 08 Dec 2025 21:05:46 GMT
• Title: Lost in Translation, Found in Embeddings: Sign Language Translation and Alignment
• Authors: Youngjoon Jang, Liliane Momeni, Zifan Jiang, Joon Son Chung, Gül Varol, Andrew Zisserman,
• Abstract summary: We develop a model for sign language understanding that performs sign language translation (SLT) and sign-subtitle alignment (SSA)<n>Our approach is built upon three components: (i) a lightweight visual backbone that captures manual and non-manual cues from human keypoints and lip-region images; (ii) a Sliding Perceiver mapping network that aggregates consecutive visual features into word-level embeddings; and (iii) a multi-task scalable training strategy that jointly optimises SLT and SSA.
• Score: 84.39962912136525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our aim is to develop a unified model for sign language understanding, that performs sign language translation (SLT) and sign-subtitle alignment (SSA). Together, these two tasks enable the conversion of continuous signing videos into spoken language text and also the temporal alignment of signing with subtitles -- both essential for practical communication, large-scale corpus construction, and educational applications. To achieve this, our approach is built upon three components: (i) a lightweight visual backbone that captures manual and non-manual cues from human keypoints and lip-region images while preserving signer privacy; (ii) a Sliding Perceiver mapping network that aggregates consecutive visual features into word-level embeddings to bridge the vision-text gap; and (iii) a multi-task scalable training strategy that jointly optimises SLT and SSA, reinforcing both linguistic and temporal alignment. To promote cross-linguistic generalisation, we pretrain our model on large-scale sign-text corpora covering British Sign Language (BSL) and American Sign Language (ASL) from the BOBSL and YouTube-SL-25 datasets. With this multilingual pretraining and strong model design, we achieve state-of-the-art results on the challenging BOBSL (BSL) dataset for both SLT and SSA. Our model also demonstrates robust zero-shot generalisation and finetuned SLT performance on How2Sign (ASL), highlighting the potential of scalable translation across different sign languages.

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