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\footnote{\url{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.

翻译：在汽车领域创建用于动态人机界面的多样且全面的手势数据集可能既具挑战性又耗时。为克服这一难题，我们提出使用由虚拟3D模型生成的合成手势数据集。我们的框架利用虚幻引擎合成逼真的手势，提供定制化选项并降低过拟合风险。为提高泛化能力，我们生成了包括手势速度、表现方式及手部形状在内的多种变体。此外，我们还模拟了不同摄像头位置与类型（如RGB、红外及深度摄像头），无需额外花费时间与成本获取这些摄像头。实验结果表明，我们提出的框架SynthoGestures\footnote{\url{https://github.com/amrgomaaelhady/SynthoGestures}}可提升手势识别准确率，并能替代或增强真实手势数据集。通过节省数据集创建的时间和精力，我们的工具加速了汽车应用手势识别系统的开发。