Multi-Density Sketch-to-Image Translation Network

• URL: http://arxiv.org/abs/2006.10649v1
• Date: Thu, 18 Jun 2020 16:21:04 GMT
• Title: Multi-Density Sketch-to-Image Translation Network
• Authors: Jialu Huang, Jing Liao, Zhifeng Tan, Sam Kwong
• Abstract summary: We propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketch-to-photo translation, and anime colorization.
• Score: 65.4028451067947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translation including sketch-to-photo and sketch-to-painting can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to micro structures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and generation of images. Moreover, our method has been successfully verified on various datasets for different applications including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.

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