# Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses

arXiv: https://arxiv.org/abs/2012.10941 · HTML: https://mlsanity.com/paper/2012.10941/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation](https://mlsanity.com/paper/2305.17714/) (2023)
- [Sign Stitching: A Novel Approach to Sign Language Production](https://mlsanity.com/paper/2405.07663/) (2024)
- [Pose-Guided Fine-Grained Sign Language Video Generation](https://mlsanity.com/paper/2409.16709/) (2024)
- [Text-Driven 3D Hand Motion Generation from Sign Language Data](https://mlsanity.com/paper/2508.15902/) (2025)
- [Pose-Guided Sign Language Video GAN with Dynamic Lambda](https://mlsanity.com/paper/2105.02742/) (2021)
- [A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation](https://mlsanity.com/paper/2308.08849/) (2023)
- [Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting](https://mlsanity.com/paper/2209.01470/) (2022)
- [SignLLM: Sign Language Production Large Language Models](https://mlsanity.com/paper/2405.10718/) (2024)
- [HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation](https://mlsanity.com/paper/2508.14345/) (2025)

## Abstract (arXiv)

Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.