Adversarial Segmentation Loss for Sketch Colorization
This addresses the challenge of sketch colorization for applications like digital art and design by enabling unpaired translation, though it is incremental as it builds on existing GAN methods.
The paper tackles the problem of generating color images from sketches without requiring paired data or user guidance by leveraging semantic segmentation to create an adversarial loss function, resulting in improvements of up to 35 points on the FID metric across multiple datasets.
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.