Abstract
Image synthesis has raised tremendous attention in both academic and industrial areas, especially for conditional and target-oriented image synthesis, such as criminal portrait and fashion design. The current studies have achieved encouraging results along this direction, but they mostly focus on class labels where spatial contents are randomly generated from latent vectors. Some recent studies have explored spatial constraints for generative models guided by semantic segmentation, but most of them are designed for scene generation and lack random variation. Such methods are not suitable for face or fashion image synthesis, where different images may share the same semantics. Different from all the current methods, we decouple the image synthesis task into three independent dimensions and propose a novel Spatially Constrained Generative Adversarial Network (SCGAN) to model it. SCGAN uses a simple yet effective way to decouple spatial constraints and attribute conditions from latent vectors, and treat them as additional controllable signals via a segmentor and a specially designed generator. Other unregulated contents are left to be generated from latent vectors. Experimentally, we provide both qualitative and quantitative results on CelebA and DeepFashion datasets to demonstrate that the proposed SCGAN is very effective in synthesizing spatially controllable and attribute-specific images with high visual quality and large variations. Our code is provided at https://github.com/jackyjsy/SCGAN.