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Recent advancements in conditional Generative Adversarial Networks (cGANs)have shown promises in label guided image synthesis. Semantic masks, such assketches and label maps, are another intuitive and effective form of guidancein image synthesis. Directly incorporating the semantic masks as constraintsdramatically reduces the variability and quality of the synthesized results. Weobserve this is caused by the incompatibility of features from different inputs(such as mask image and latent vector) of the generator. To use semantic masksas guidance whilst providing realistic synthesized results with fine details,we propose to use mask embedding mechanism to allow for a more efficientinitial feature projection in the generator. We validate the effectiveness ofour approach by training a mask guided face generator using CELEBA-HQ dataset.We can generate realistic and high resolution facial images up to theresolution of 512*512 with a mask guidance. Our code is publicly available.

Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images