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Towards Large-Pose Face Frontalization in the Wild
ICCV 2017 | Despite recent advances in deep face recognition, severe accuracy drops are observed under large pose variations. Learning pose-invariant features is feasible but needs expensively labeled data. In this work, we focus on frontalizing faces in the wild under various head poses. We propose a novel deep 3D morphable model (3DMM)-conditioned face frontalization generative adversarial network, termed as FF-GAN, to generate neutral head pose face images showing photo-realistic visual effects.
Collaborators: Xi Yin, Kihyuk Sohn, Xiaoming Liu, Manmohan Chandraker
