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SignLLM: Sign Language Production Large Language Models

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arxiv 2405.10718 v3 pith:AYCVQPKA submitted 2024-05-17 cs.CV cs.CL

Sen Fang , Chen Chen , Lei Wang , Ce Zheng , Chunyu Sui , Yapeng Tian This is my paper · ORCID
classification cs.CVcs.CL
keywords languagesignsignllmmodelmultilingualdatadatasetinformation
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In this paper, we propose SignLLM, a multilingual Sign Language Production (SLP) large language model, which includes two novel multilingual SLP modes MLSF and Prompt2LangGloss that allow sign language gestures generation from query texts input and question-style prompts input respectively. Both modes can use a new RL loss based on reinforcement learning and a new RL module named Priority Learning Channel. These RL components can accelerate the training by enhancing the model's capability to sample high-quality data. To train SignLLM, we introduce Prompt2Sign, a comprehensive multilingual sign language dataset, which builds from public data, including American Sign Language (ASL) and seven others. This dataset standardizes information by extracting pose information from sign language videos into a unified compressed format. We extensively evaluate SignLLM, demonstrating that our model achieves state-of-the-art performance on SLP tasks across eight sign languages.

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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. Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation

    cs.HC 2025-08 conditional novelty 6.0 of 10

    A new 60-word Bengali sign language instruction dataset and a sign-parameter-infused prompting method that modestly improves VLM-generated instructions on most text-matching metrics for larger models.

  2. EmoSign: A Multimodal Dataset for Understanding Emotions in American Sign Language

    cs.CV 2025-05 conditional novelty 6.0 of 10

    EmoSign is a 200-clip American Sign Language video dataset with native-signer sentiment and emotion labels plus baseline multimodal LLM results showing poor visual-only emotion recognition.

  3. Physics-Informed Diffusion for Biomechanically Plausible 3D Sign Language Generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A physics-informed diffusion model with a differentiable anatomical refiner and contrastive gloss-pose alignment improves realism and back-translation scores for 3D sign language generation, at least against its own n...

  4. Exploring Pose-based Sign Language Translation: Ablation Studies and Attention Insights

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Pose normalization based on the signer's signing space substantially improves gloss-free sign language translation with a T5 model, while interpolation and augmentation give smaller, less certain gains.

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