A high fidelity synthetic face framework for computer vision

• URL: http://arxiv.org/abs/2007.08364v1
• Date: Thu, 16 Jul 2020 14:40:28 GMT
• Title: A high fidelity synthetic face framework for computer vision
• Authors: Tadas Baltrusaitis, Erroll Wood, Virginia Estellers, Charlie Hewitt, Sebastian Dziadzio, Marek Kowalski, Matthew Johnson, Thomas J. Cashman, and Jamie Shotton
• Abstract summary: We propose synthesizing facial data, including ground truth annotations, 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.
• Score: 10.679578971210912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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