We propose a new method for producing color images from sketches. Current solutions in sketch colorization either necessitate additional user instruction or are restricted to the "paired" translation strategy. We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function. The proposed loss function is compatible with any GAN model. Our method is not restricted to datasets with segmentation labels and can be applied to unpaired translation tasks as well. Using qualitative, and quantitative analysis, and based on a user study, we demonstrate the efficacy of our method on four distinct image datasets. On the FID metric, our model improves the baseline by up to 35 points. Our code, pretrained models, scripts to produce newly introduced datasets and corresponding sketch images are available at https://github.com/giddyyupp/AdvSegLoss.

翻译：我们提出一个新的方法来从草图中制作彩色图像。 目前草图颜色化的解决方案要么需要额外的用户指令,要么局限于“paired”翻译策略。 我们利用一般用途全光分割网的语义图像分割法产生额外的对抗性损失功能。 拟议的损失功能与任何GAN模型不相容。 我们的方法不局限于带有分解标签的数据集,也可以适用于无偏差的翻译任务。 使用定性和定量分析,并根据用户研究,我们在四种不同的图像数据集上展示了我们方法的功效。 在FID 指标上,我们的模型将基线改进了多达35个点。 我们的代码、 预培训模型、 生成新引入数据集的脚本 和相应的草图图像可在 https://github./ comddyyupp/AdvSegLos 上查阅。