Interactive Cartoonization with Controllable Perceptual Factors

• URL: http://arxiv.org/abs/2212.09555v1
• Date: Mon, 19 Dec 2022 15:45:47 GMT
• Title: Interactive Cartoonization with Controllable Perceptual Factors
• Authors: Namhyuk Ahn, Patrick Kwon, Jihye Back, Kibeom Hong, Seungkwon Kim
• Abstract summary: We propose a novel solution with editing features of texture and color based on the cartoon creation process. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate diverse and controllable color translation.
• Score: 5.8641445422054765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cartoonization is a task that renders natural photos into cartoon styles. Previous deep cartoonization methods only have focused on end-to-end translation, which may hinder editability. Instead, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate diverse and controllable color translation. To the best of our knowledge, our work is the first deep approach to control the cartoonization at inference while showing profound quality improvement over to baselines.

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