  Image generation has raised tremendous attention in both academic and
industrial areas, especially for the conditional and target-oriented image
generation, such as criminal portrait and fashion design. Although the current
studies have achieved preliminary results along this direction, they always
focus on class labels as the condition where spatial contents are randomly
generated from latent vectors. Edge details are usually blurred since spatial
information is difficult to preserve. In light of this, we propose a novel
Spatially Constrained Generative Adversarial Network (SCGAN), which decouples
the spatial constraints from the latent vector and makes these constraints
feasible as additional controllable signals. To enhance the spatial
controllability, a generator network is specially designed to take a semantic
segmentation, a latent vector and an attribute-level label as inputs step by
step. Besides, a segmentor network is constructed to impose spatial constraints
on the generator. Experimentally, we provide both visual and quantitative
results on CelebA and DeepFashion datasets, and demonstrate that the proposed
SCGAN is very effective in controlling the spatial contents as well as
generating high-quality images.
