# SynthoGestures: A Novel Framework for Synthetic Dynamic Hand Gesture Generation for Driving Scenarios

arXiv: https://arxiv.org/abs/2309.04421 · HTML: https://mlsanity.com/paper/2309.04421/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Procedural Generation of Videos to Train Deep Action Recognition Networks](https://mlsanity.com/paper/1612.00881/) (2016)
- [Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/1901.10323/) (2019)
- [Designing Mid-Air Haptic Gesture Controlled User Interfaces for Cars](https://mlsanity.com/paper/2005.08535/) (2020)

## Abstract (arXiv)

Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by virtual 3D models. Our framework utilizes Unreal Engine to synthesize realistic hand gestures, offering customization options and reducing the risk of overfitting. Multiple variants, including gesture speed, performance, and hand shape, are generated to improve generalizability. In addition, we simulate different camera locations and types, such as RGB, infrared, and depth cameras, without incurring additional time and cost to obtain these cameras. Experimental results demonstrate that our proposed framework, SynthoGestures (https://github.com/amrgomaaelhady/SynthoGestures), improves gesture recognition accuracy and can replace or augment real-hand datasets. By saving time and effort in the creation of the data set, our tool accelerates the development of gesture recognition systems for automotive applications.