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