  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.
