Collaging Class-specific GANs for Semantic Image Synthesis

International Conference on Computer Vision (ICCV'21)

Publication date: October 13, 2021

Yuheng Li, Yijun Li, Jingwan (Cynthia) Lu, Eli Shechtman, Yong Jae Lee, Krishna Kumar Singh, Jingwan (Cynthia) Lu

We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. The base generator generates high quality images based on a segmentation map. To further improve the quality of different objects, we create a bank of Generative Adversarial Networks (GANs) by separately training class-specific models. This has several benefits including -- dedicated weights for each class; centrally aligned data for each model; additional training data from other sources, potential of higher resolution and quality; and easy manipulation of a specific object in the scene. Experiments show that our approach can generate high quality images in high resolution while having flexibility of object-level control by using class-specific generators.

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