# All You Need In Sign Language Production arXiv: https://arxiv.org/abs/2201.01609 ยท HTML: https://mlsanity.com/paper/2201.01609/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 50 of its 173 references that are in the map. - First posted on arXiv 5 Jan 2022. ## Builds on (most cited first, 30 of 50) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016) - [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014) - [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014) - [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015) - [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017) - [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015) - [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016) - [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016) - [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013) - [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017) - [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017) - [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019) - [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018) - [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015) - [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017) - [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017) - [Unsupervised Learning for Physical Interaction through Video Prediction](https://mlsanity.com/paper/1605.07157/) (2016) - [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017) - [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016) - [Photographic Image Synthesis with Cascaded Refinement Networks](https://mlsanity.com/paper/1707.09405/) (2017) - [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https://mlsanity.com/paper/1506.05751/) (2015) - [DRAW: A Recurrent Neural Network For Image Generation](https://mlsanity.com/paper/1502.04623/) (2015) - [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020) - [Decomposing Motion and Content for Natural Video Sequence Prediction](https://mlsanity.com/paper/1706.08033/) (2017) ## Built on by (most cited first, 8 of 8) - [TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation](https://mlsanity.com/paper/2206.07117/) (2022) - [Beyond Gloss: A Hand-Centric Framework for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2507.23575/) (2025) - [iSign: A Benchmark for Indian Sign Language Processing](https://mlsanity.com/paper/2407.05404/) (2024) - [Hierarchical Feature Alignment for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2507.06732/) (2025) - [Word separation in continuous sign language using isolated signs and post-processing](https://mlsanity.com/paper/2204.00923/) (2022) - [A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences](https://mlsanity.com/paper/2207.07619/) (2022) - [FEA-SLT: A Gloss-Free End-to-End Framework for Facial-Expression-Aware Sign Language Translation](https://mlsanity.com/paper/2601.03549/) (2026) - [KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation](https://mlsanity.com/paper/2605.09572/) (2026) ## Abstract (arXiv) Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental. To this end, sign language recognition and production are two necessary parts for making such a two-way system. Sign language recognition and production need to cope with some critical challenges. In this survey, we review recent advances in Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented.