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Analysis of faces is one of the core applications of computer vision, withtasks ranging from landmark alignment, head pose estimation, expressionrecognition, and face recognition among others. However, building reliablemethods requires time-consuming data collection and often even moretime-consuming manual annotation, which can be unreliable. In our work wepropose synthesizing such facial data, including ground truth annotations thatwould be almost impossible to acquire through manual annotation at theconsistency and scale possible through use of synthetic data. We use aparametric face model together with hand crafted assets which enable us togenerate training data with unprecedented quality and diversity (varying shape,texture, expression, pose, lighting, and hair).

A high fidelity synthetic face framework for computer vision