Outline Colorization through Tandem Adversarial Networks

2017/04/28 by Kevin Frans, Frans, Kevin · 2 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis

paper · pdf · doi:10.48550/arxiv.1704.08834

openalex publication_date 2017/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

When creating digital art, coloring and shading are often time consuming tasks that follow the same general patterns. A solution to automatically colorize raw line art would have many practical applications. We propose a setup utilizing two networks in tandem: a color prediction network based only on outlines, and a shading network conditioned on both outlines and a color scheme. We present processing methods to limit information passed in the color scheme, improving generalization. Finally, we demonstrate natural-looking results when colorizing outlines from scratch, as well as from a messy, user-defined color scheme.

Citations

• Generative Adversarial Networks
• Learning Representations for Automatic Colorization

Cited by

• GANime: Generating Anime and Manga Character Drawings from Sketches with Deep Learning
• The power of pictures: using ML assisted image generation to engage the\n crowd in complex socioscientific problems

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