CVPR 2023 – Interactive Cartoonization with Controllable Perceptual Factors

In this episode we discuss Interactive Cartoonization with Controllable Perceptual Factors
by Namhyuk Ahn, Patrick Kwon, Jihye Back, Kibeom Hong, Seungkwon Kim. The paper proposes a new method for cartoonization, which involves rendering natural photos into cartoon styles with editing features of texture and color. The proposed method uses a model architecture with separate decoders for texture and color, and introduces a texture controller to generate diverse cartoon textures. Additionally, an HSV color augmentation is used to induce the networks to generate diverse and controllable color translation, resulting in profound quality improvement over baselines. This is the first deep approach that allows control of the cartoonization at inference.