Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization

arXiv:2208.01587v4 Announce Type: replace
Abstract: Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct …

abstract advanced adversarial arxiv attention cartoon challenge clear color cs.cv etc gans generative generative adversarial networks image image-to-image image-to-image translation networks perspective texture transfer translation type unsupervised