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We propose a novel end-to-end semi-supervised adversarial framework togenerate photorealistic face images of new identities with wide ranges ofexpressions, poses, and illuminations conditioned by a 3D morphable model.Previous adversarial style-transfer methods either supervise their networkswith large volume of paired data or use unpaired data with a highlyunder-constrained two-way generative framework in an unsupervised fashion. Weintroduce pairwise adversarial supervision to constrain two-way domainadaptation by a small number of paired real and synthetic images for trainingalong with the large volume of unpaired data. Extensive qualitative andquantitative experiments are performed to validate our idea. Generated faceimages of new identities contain pose, lighting and expression diversity andqualitative results show that they are highly constraint by the synthetic inputimage while adding photorealism and retaining identity information. We combineface images generated by the proposed method with the real data set to trainface recognition algorithms. We evaluated the model on two challenging datasets: LFW and IJB-A. We observe that the generated images from our frameworkconsistently improves over the performance of deep face recognition networktrained with Oxford VGG Face dataset and achieves comparable results to thestate-of-the-art.

Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model