# Sign language production

Area: [Diffusion models](https://mlsanity.com/diffusion/index.md). HTML: https://mlsanity.com/diffusion/sign-language-production/

## Key facts

- Sign language production: 15 papers on arXiv since 2022 (data through 14 Oct 2026).
- 7 of its 15 mapped papers (47%) cite “G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model” (2022, arXiv 2208.09141).
- 4 of its 15 mapped papers (27%) cite “Neural Sign Actors: A diffusion model for 3D sign language production from text” (2023, arXiv 2312.02702).

## Most cited here

- [G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model](https://mlsanity.com/paper/2208.09141/) (2022), cited by 7 papers here
- [Neural Sign Actors: A diffusion model for 3D sign language production from text](https://mlsanity.com/paper/2312.02702/) (2023), cited by 4 papers here
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023), cited by 2 papers here
- [Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observation](https://mlsanity.com/paper/2411.16810/) (2024), cited by 2 papers here
- [Advanced Sign Language Video Generation with Compressed and Quantized Multi-Condition Tokenization](https://mlsanity.com/paper/2506.15980/) (2025), cited by 2 papers here
- [Text-Driven Diffusion Model for Sign Language Production](https://mlsanity.com/paper/2503.15914/) (2025), cited by 1 papers here
- [Text2Sign Diffusion: A Generative Approach for Gloss-Free Sign Language Production](https://mlsanity.com/paper/2509.10845/) (2025), cited by 1 papers here