# Spatially Constrained GAN for Face and Fashion Synthesis

## TL;DR

This paper introduces SCGAN, a novel GAN architecture that improves spatial content control in face and fashion image synthesis by decoupling spatial constraints from latent vectors, resulting in higher quality and more controllable images.

## Contribution

The paper proposes a new spatially constrained GAN that separates spatial constraints from latent vectors, enhancing controllability and image quality in conditional image synthesis.

## Key findings

- Effective spatial control demonstrated on CelebA and DeepFashion datasets.
- High-quality image generation with improved spatial content preservation.
- Quantitative and visual results confirm the method's superiority over existing approaches.

## Abstract

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.