# SignDiff: Diffusion Model for American Sign Language Production

arXiv: https://arxiv.org/abs/2308.16082 · HTML: https://mlsanity.com/paper/2308.16082/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Sign language production, one of 14 directions in Diffusion models.
- Builds on 49 of its 79 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Placed in

- Diffusion models / [Sign language production](https://mlsanity.com/diffusion/sign-language-production/)

## Builds on (most cited first, 30 of 49)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [Generating Videos with Scene Dynamics](https://mlsanity.com/paper/1609.02612/) (2016)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Neural Voice Puppetry: Audio-driven Facial Reenactment](https://mlsanity.com/paper/1912.05566/) (2019)
- [Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning](https://mlsanity.com/paper/2004.11660/) (2020)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [3D Hand Shape and Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1903.00812/) (2019)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [Progressive Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/1904.03349/) (2019)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)

## Built on by (most cited first, 25 of 25)

- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [A Simple Baseline for Spoken Language to Sign Language Translation with 3D Avatars](https://mlsanity.com/paper/2401.04730/) (2024)
- [Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production](https://mlsanity.com/paper/2412.13609/) (2024)
- [Pose-Guided Fine-Grained Sign Language Video Generation](https://mlsanity.com/paper/2409.16709/) (2024)
- [SignX: Continuous Sign Recognition in Compact Pose-Rich Latent Space](https://mlsanity.com/paper/2504.16315/) (2025)
- [SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work](https://mlsanity.com/paper/2508.06951/) (2025)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [Modelling the Distribution of Human Motion for Sign Language Assessment](https://mlsanity.com/paper/2408.10073/) (2024)
- [Stable Signer: Hierarchical Sign Language Generative Model](https://mlsanity.com/paper/2512.04048/) (2025)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [M3T: Discrete Multi-Modal Motion Tokens for Sign Language Production](https://mlsanity.com/paper/2603.23617/) (2026)
- [StreamFlow: Theory, Algorithm, and Implementation for High-Efficiency Rectified Flow Generation](https://mlsanity.com/paper/2511.22009/) (2025)
- [Context-Aware Autoregressive Diffusion for Gloss-Wise Sign Language Production](https://mlsanity.com/paper/2606.21234/) (2026)
- [Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model](https://mlsanity.com/paper/2412.07333/) (2024)
- [Human-Centered Editable Speech-to-Sign-Language Generation via Streaming Conformer-Transformer and Resampling Hook](https://mlsanity.com/paper/2506.14677/) (2025)
- [Towards Skeletal and Signer Noise Reduction in Sign Language Production via Quaternion-Based Pose Encoding and Contrastive Learning](https://mlsanity.com/paper/2508.14574/) (2025)
- [EASL: Multi-Emotion Guided Semantic Disentanglement for Expressive Sign Language Generation](https://mlsanity.com/paper/2511.22135/) (2025)
- [RAC: Rectified Flow Auto Coder](https://mlsanity.com/paper/2603.05925/) (2026)
- [SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages](https://mlsanity.com/paper/2605.01720/) (2026)
- [Physics-Informed Diffusion for Biomechanically Plausible 3D Sign Language Generation](https://mlsanity.com/paper/2607.14836/) (2026)
- [SignRefine: Adapting Foundational Video Models for Sign Language Generation](https://mlsanity.com/paper/2609.08496/) (2026)
- [SignGPT: Toward LLM-Mediated Sign Language Interaction through Gloss-Free Translation and Generation](https://mlsanity.com/paper/2609.21709/) (2026)
- [PHOSA: Photorealistic 3D Sign Avatar Modeling and Benchmark](https://mlsanity.com/paper/2609.29292/) (2026)
- [RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots](https://mlsanity.com/paper/2609.32250/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)

## Abstract (arXiv)

In this paper, we propose a dual-condition diffusion pre-training model named SignDiff that can generate human sign language speakers from a skeleton pose. SignDiff has a novel Frame Reinforcement Network called FR-Net, similar to dense human pose estimation work, which enhances the correspondence between text lexical symbols and sign language dense pose frames, reduces the occurrence of multiple fingers in the diffusion model. In addition, we propose a new method for American Sign Language Production (ASLP), which can generate ASL skeletal pose videos from text input, integrating two new improved modules and a new loss function to improve the accuracy and quality of sign language skeletal posture and enhance the ability of the model to train on large-scale data. We propose the first baseline for ASL production and report the scores of 17.19 and 12.85 on BLEU-4 on the How2Sign dev/test sets. We evaluated our model on the previous mainstream dataset PHOENIX14T, and the experiments achieved the SOTA results. In addition, our image quality far exceeds all previous results by 10 percentage points in terms of SSIM.