|
|
|
|
|
|
|
|
|
|
| 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. |
|
|
 |
 |
| Visual comparison of segmentation map to image synthesis results on the ADE20K bedroom (512x512), full human body (512x512), and Cityscapes (1024x512) datasets. |
 |
| Multi-modal synthesis results on bedroom, human and cityscapes by our base model. Each row shows multiple generations for same semantic mask. |
 |
| Our CollageGAN model takes advantage of class-specific generators to provide more details to specific classes in the image generated by the base model. |
 |
| Images in the red box are real images. Here we use the bed and uppercloth specific generator to replace the original objects. |
 |
| The mixed-resolution result. Here the base image is first resized to 4096x4096 and then face region is composited by high quality face generated from face specific model. |
 |
Yuheng Li, Yijun Li, Jingwan Lu, Eli Shechtman, Yong Jae Lee, Krishna Kumar Singh Collaging Class-specific GANs for Semantic Image Synthesis |
AcknowledgementsThis template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here. |