Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision

european conference on computer vision, pp. 558-574, 2020.

Cited by: 0|Bibtex|Views21|DOI:https://doi.org/10.1007/978-3-030-58610-2_33
Other Links: arxiv.org|academic.microsoft.com

Abstract:

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training. We propose a novel Flow-based Feature Warping Model (FFWM) which can learn to synthesize photo-realistic and i...More

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