Stuff segmentation and non-photorealistic rendering with generative adversarial networks - Choy, Jin Xiang (Nanyang Technological University, 2020)
Generative Adversarial Networks (GANs) have shown impressive results in a variety of image generation tasks in recent years, including rendering photorealistic images with artistic styles. However, current work in transforming images have mostly focused on either transforming the whole image, or on the thing classes. There have been little attention on the artistic rendering of only the stuff classes of images. Current possible methods of performing painting of specific image regions also result in unnatural boundaries between painted and non-painted regions. Therefore, we aim to develop an end-to-end model for the novel task of Non-Photorealistically Rendering the stuff of images.