SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation

arXiv:2609.21709 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Project page: https://signgpt-demo.github.io/sign-language-interaction-demo

Terminology

• Embeddings — 28× in this paper · explained in 2 episodes
• Ablation — 22× in this paper · explained in 1 episode
• Quantization — 22× in this paper · explained in 12 episodes
• Sign Language Translation — 18× in this paper
• Alignment — 14× in this paper · explained in 1 episode
• Sign Language Generation — 12× in this paper
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• Ablation study — 10× in this paper · explained in 1 episode
• Adaptation — 8× in this paper · explained in 1 episode
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• American Sign Language — 5× in this paper
• Tokenization — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Decode — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Conference on Automatic Face and Gesture Recognition — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Through Asymmetric Multi-Token Prediction — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Cross-lingual Transfer — 1× in this paper · explained in 2 episodes
• Dynamic Time Warping on Joint Position Errors — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Glossfree Heterogeneous Motion-aware Language Model — 1× in this paper
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Per Joint Position Error — 1× in this paper
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• The Llama 3 Herd of Models
• SIGNER: Temporally Grounded Sign Language Generation via Time-Resolved Conditioning
• Uni-Sign: Toward Unified Sign Language Understanding at Scale
• VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension
• HumanTOMATO: Text-aligned Whole-body Motion Generation
• Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation
• Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs

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