A high fidelity synthetic face framework for computer vision
This work addresses the data collection bottleneck for researchers and practitioners in computer vision, particularly for face analysis applications, and is incremental as it builds on existing parametric face models and hand-crafted assets.
The paper tackles the problem of time-consuming and unreliable manual annotation for face analysis tasks in computer vision by proposing a synthetic face data generation framework, which produces high-quality and diverse training data with ground truth annotations.
Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotation, which can be unreliable. In our work we propose synthesizing such facial data, including ground truth annotations that would be almost impossible to acquire through manual annotation at the consistency and scale possible through use of synthetic data. We use a parametric face model together with hand crafted assets which enable us to generate training data with unprecedented quality and diversity (varying shape, texture, expression, pose, lighting, and hair).