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

TL;DR

SynthoGestures is a framework that uses Unreal Engine to generate diverse, realistic synthetic hand gesture datasets for automotive human-machine interfaces, enhancing recognition accuracy and reducing data collection costs.

Contribution

We introduce SynthoGestures, a novel synthetic data generation framework that creates customizable, realistic hand gesture datasets for automotive applications using virtual 3D models.

Findings

Improves gesture recognition accuracy with synthetic data

Reduces time and cost of dataset creation

Enhances generalizability with diverse gesture variants

Abstract

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.