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An Efficient Sign Language Translation Using Spatial Configuration and Motion Dynamics with LLMs

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arxiv 2408.10593 v3 pith:LXODLPB4 submitted 2024-08-20 cs.CL cs.CV

Eui Jun Hwang , Sukmin Cho , Junmyeong Lee , Jong C. Park This is my paper · ORCID
classification cs.CLcs.CV
keywords languagesignspatialtranslationmotionspamodynamicsencoders
verification ladder T0 review T1 audit T2 compute T3 formal
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Gloss-free Sign Language Translation (SLT) converts sign videos directly into spoken language sentences without relying on glosses. Recently, Large Language Models (LLMs) have shown remarkable translation performance in gloss-free methods by harnessing their powerful natural language generation capabilities. However, these methods often rely on domain-specific fine-tuning of visual encoders to achieve optimal results. By contrast, this paper emphasizes the importance of capturing the spatial configurations and motion dynamics inherent in sign language. With this in mind, we introduce Spatial and Motion-based Sign Language Translation (SpaMo), a novel LLM-based SLT framework. The core idea of SpaMo is simple yet effective. We first extract spatial and motion features using off-the-shelf visual encoders and then input these features into an LLM with a language prompt. Additionally, we employ a visual-text alignment process as a warm-up before the SLT supervision. Our experiments demonstrate that SpaMo achieves state-of-the-art performance on two popular datasets, PHOENIX14T and How2Sign.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Attention-Steered Vision-Language Models for Sign Language Translation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    AttnSign adds spatial attention supervision and motion-cadence reinforcement learning to a VLM, improving sign language translation accuracy on How2Sign and OpenASL.

  2. Bridging Sign and Spoken Languages: Pseudo Gloss Generation for Sign Language Translation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    LLM-generated pseudo glosses, reordered via weak video supervision, enable sign language translation that rivals gloss-supervised models while needing only 30 gloss examples.

  3. Leveraging Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    A prompting method that links signs to short text descriptions lets large language models translate English and Italian into sign language glosses, beating prior models in low-data settings.

  4. ViPo-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation

    cs.CV 2026-07 conditional novelty 4.5 of 10

    Fusing spatio-temporal RGB and OpenPose features via intra- and cross-modal temporal modeling plus contrastive LLM fine-tuning yields new gloss-free SOTA on PHOENIX14T (BLEU-4 27.10) and CSL-Daily (BLEU-4 25.85).

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