Adversarial Segmentation Loss for Sketch Colorization

TL;DR

This paper presents a novel adversarial segmentation loss for sketch colorization that leverages semantic segmentation to improve GAN-based image translation, effective across various datasets and unpaired scenarios.

Contribution

Introduces a segmentation-based adversarial loss that enhances sketch colorization in GANs, applicable to unpaired datasets without requiring segmentation labels.

Findings

Improves FID scores by up to 35 points across datasets.

Effective in scene, outdoor, and illustration image translation.

Compatible with any baseline GAN model.

Abstract

We introduce a new method for generating color images from sketches or edge maps. Current methods either require some form of additional user-guidance or are limited to the "paired" translation approach. We argue that segmentation information could provide valuable guidance for sketch colorization. To this end, we propose to leverage semantic image segmentation, as provided by a general purpose panoptic segmentation network, to create an additional adversarial loss function. Our loss function can be integrated to any baseline GAN model. Our method is not limited to datasets that contain segmentation labels, and it can be trained for "unpaired" translation tasks. We show the effectiveness of our method on four different datasets spanning scene level indoor, outdoor, and children book illustration images using qualitative, quantitative and user study analysis. Our model improves its baseline up to 35 points on the FID metric. Our code and pretrained models can be found at https://github.com/giddyyupp/AdvSegLoss.