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Semi-Latent GAN: Learning to generate and modify facial images from attributes
[article]
2017
arXiv
pre-print
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of these two problems (generation or manipulation), but not both coherently. This paper proposes a novel model that learns how to both generate and modify the facial image from high-level semantic attributes. Our key idea is to formulate a Semi-Latent Facial Attribute Space (SL-FAS) to systematically learn relationship
arXiv:1704.02166v1
fatcat:tfw76sqzybcfnbokju5obx2jim