  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 illumination preserving frontal images with
illumination inconsistent supervision. Specifically, an Illumination Preserving
Module (IPM) is proposed to learn illumination preserving image synthesis from
illumination inconsistent image pairs. IPM includes two pathways which
collaborate to ensure the synthesized frontal images are illumination
preserving and with fine details. Moreover, a Warp Attention Module (WAM) is
introduced to reduce the pose discrepancy in the feature level, and hence to
synthesize frontal images more effectively and preserve more details of profile
images. The attention mechanism in WAM helps reduce the artifacts caused by the
displacements between the profile and the frontal images. Quantitative and
qualitative experimental results show that our FFWM can synthesize
photo-realistic and illumination preserving frontal images and performs
favorably against the state-of-the-art results.
