Mangan: Assisting Colorization Of Manga Characters Concept Art Using Conditional GAN

Silva, Felipe Coelho; Castro, Paulo André Lima de; Ricardo, Helio; Marujo, Ernesto Cordeiro

doi:10.1109/icip.2019.8803667

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…The MANGAN approach [20] also uses a GAN, where the authors came up with their own methodology to extract the line art from colorized images in order to train the network. Their line art extraction contains a lot of noise and generated lines are thicker than usual.…”

Section: Literature Reviewmentioning

confidence: 99%

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

Rodrigues

Clua

Vitor

2022

2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

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This work proposes a complete methodology to colorize images of Fakemon, anime-style monster-like creatures.In addition, we propose algorithms to extract the line art from colorized images as well as to extract color hints. Our work is the first in the literature to use automatic color hint extraction, to train the networks specifically with anime-styled creatures and to combine the Pix2Pix and CycleGAN approaches, two different generative adversarial networks that create a single final result. Visual results of the colorizations are feasible but there is still room for improvement.

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Section: Literature Reviewmentioning

confidence: 99%

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

Rodrigues

Clua

Vitor

2022

2022 21st Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)

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“…While these can be useful for visual abstraction (e.g., preserving and enhancing local shapes), it is difficult to consider semantic constraints and capture specific styles. The other approach is to train a network that automatically generates artistic-like drawings from facial images [31][32][33][34]. In these problem settings, training a network requires pairs of facial images and portraits.…”

Section: Portrait Renderingmentioning

confidence: 99%

DualFace: Two-stage drawing guidance for freehand portrait sketching

et al. 2021

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Special skills are required in portrait painting, such as imagining geometric structures and facial detail for final portrait designs. This makes it a difficult task for users, especially novices without prior artistic training, to draw freehand portraits with high-quality details. In this paper, we propose dualFace, a portrait drawing interface to assist users with different levels of drawing skills to complete recognizable and authentic face sketches. Inspired by traditional artist workflows for portrait drawing, dualFace gives two-stages of drawing assistance to provide global and local visual guidance. The former helps users draw contour lines for portraits (i.e., geometric structure), and the latter helps users draw details of facial parts, which conform to the user-drawn contour lines. In the global guidance stage, the user draws several contour lines, and dualFace then searches for several relevant images from an internal database and displays the suggested face contour lines on the background of the canvas. In the local guidance stage, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, and then use the synthesized results as detailed drawing guidance. We conducted a user study to verify the effectiveness of dualFace, which confirms that dualFace significantly helps users to produce a detailed portrait sketch.

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“…Silva et al [19] proposed a hint-based semi-automatic manga colorization method that uses cGAN as its model. The difference between ManGAN and our method is that ManGAN takes a pair of line-art and color-hint images as input, whereas ours takes a pair of screentones and flat-colored images.…”

Section: Manga Colorizationmentioning

confidence: 99%

Painting Style-Aware Manga Colorization Based On Generative Adversarial Networks

Shimizu

Furuta

Ouyang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, which incurs high labor costs. Although automatic colorization methods have been recently proposed, most of them are designed for illustrations, not for comics. Unlike illustrations, since comics are composed of many consecutive images, the painting style must be consistent.To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data. The proposed method takes a pair of a screen tone image and a flat colored image as input, and outputs a colorized image. Experiments show that the proposed method achieves better performance than the existing alternatives.

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Mangan: Assisting Colorization Of Manga Characters Concept Art Using Conditional GAN

Cited by 15 publications

References 9 publications

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

DualFace: Two-stage drawing guidance for freehand portrait sketching

Painting Style-Aware Manga Colorization Based On Generative Adversarial Networks

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