Sketch-to-Art: Synthesizing Stylized Art Images From Sketches

• URL: http://arxiv.org/abs/2002.12888v3
• Date: Fri, 2 Oct 2020 17:20:25 GMT
• Title: Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
• Authors: Bingchen Liu, Kunpeng Song, Ahmed Elgammal
• Abstract summary: We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, the model can synthesize meaningful details with colors and textures.
• Score: 23.75420342238983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new approach for synthesizing fully detailed art-stylized images from sketches. Given a sketch, with no semantic tagging, and a reference image of a specific style, the model can synthesize meaningful details with colors and textures. The model consists of three modules designed explicitly for better artistic style capturing and generation. Based on a GAN framework, a dual-masked mechanism is introduced to enforce the content constraints (from the sketch), and a feature-map transformation technique is developed to strengthen the style consistency (to the reference image). Finally, an inverse procedure of instance-normalization is proposed to disentangle the style and content information, therefore yields better synthesis performance. Experiments demonstrate a significant qualitative and quantitative boost over baselines based on previous state-of-the-art techniques, adopted for the proposed process.

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