# HandCraft: Dynamic Sign Generation for Synthetic Data Augmentation

arXiv: https://arxiv.org/abs/2508.14345 · HTML: https://mlsanity.com/paper/2508.14345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Data Augmentation Can Improve Robustness](https://mlsanity.com/paper/2111.05328/) (2021)
- [Back to MLP: A Simple Baseline for Human Motion Prediction](https://mlsanity.com/paper/2207.01567/) (2022)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages](https://mlsanity.com/paper/2110.05877/) (2021)
- [Ham2Pose: Animating Sign Language Notation into Pose Sequences](https://mlsanity.com/paper/2211.13613/) (2022)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [SignAvatar: Sign Language 3D Motion Reconstruction and Generation](https://mlsanity.com/paper/2405.07974/) (2024)
- [Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses](https://mlsanity.com/paper/2012.10941/) (2020)
- [Pose-Guided Fine-Grained Sign Language Video Generation](https://mlsanity.com/paper/2409.16709/) (2024)
- [Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition](https://mlsanity.com/paper/2407.14224/) (2024)
- [Diverse 3D Human Pose Generation in Scenes based on Decoupled Structure](https://mlsanity.com/paper/2406.05691/) (2024)

## Abstract (arXiv)

Sign Language Recognition (SLR) models face significant performance limitations due to insufficient training data availability. In this article, we address the challenge of limited data in SLR by introducing a novel and lightweight sign generation model based on CMLPe. This model, coupled with a synthetic data pretraining approach, consistently improves recognition accuracy, establishing new state-of-the-art results for the LSFB and DiSPLaY datasets using our Mamba-SL and Transformer-SL classifiers. Our findings reveal that synthetic data pretraining outperforms traditional augmentation methods in some cases and yields complementary benefits when implemented alongside them. Our approach democratizes sign generation and synthetic data pretraining for SLR by providing computationally efficient methods that achieve significant performance improvements across diverse datasets.