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Creating a diverse and comprehensive dataset of hand gestures for dynamichuman-machine interfaces in the automotive domain can be challenging andtime-consuming. To overcome this challenge, we propose using synthetic gesturedatasets generated by virtual 3D models. Our framework utilizes Unreal Engineto synthesize realistic hand gestures, offering customization options andreducing the risk of overfitting. Multiple variants, including gesture speed,performance, and hand shape, are generated to improve generalizability. Inaddition, we simulate different camera locations and types, such as RGB,infrared, and depth cameras, without incurring additional time and cost toobtain these cameras. Experimental results demonstrate that our proposedframework,SynthoGestures\footnote{\url{https://github.com/amrgomaaelhady/SynthoGestures}},improves gesture recognition accuracy and can replace or augment real-handdatasets. By saving time and effort in the creation of the data set, our toolaccelerates the development of gesture recognition systems for automotiveapplications.

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