Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss (2019-08-16T00:00:00.000000Z)

TL;DR

A GAN approach of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image is proposed and a novel network structure called SECat is proposed, which makes the generator properly colorize even small features such as eyes.

Abstract

Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.

Authors

Ho Young Jhoo

2 papers

Hyunsu Kim

1 papers

Eunhyeok Park

1 papers

TL;DR

Abstract

Authors

References36 items

A Style-Based Generator Architecture for Generative Adversarial Networks

Two-stage sketch colorization

Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language

User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks

Learning to Color from Language

Coloring with Words: Guiding Image Colorization Through Text-Based Palette Generation

Language-Based Image Editing with Recurrent Attentive Models

Squeeze-and-Excitation Networks

Towards the Automatic Anime Characters Creation with Generative Adversarial Networks

Semantic Image Synthesis via Adversarial Learning

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

cGAN-Based Manga Colorization Using a Single Training Image

Comicolorization: semi-automatic manga colorization

Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

Real-time user-guided image colorization with learned deep priors

Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

Scribbler: Controlling Deep Image Synthesis with Sketch and Color

Image-to-Image Translation with Conditional Adversarial Networks

Aggregated Residual Transformations for Deep Neural Networks

Conditional Image Synthesis with Auxiliary Classifier GANs

Let there be color!

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Rethinking the Inception Architecture for Computer Vision

Illustration2Vec: a semantic vector representation of illustrations

U-Net: Convolutional Networks for Biomedical Image Segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization

A Computational Approach to Edge Detection

GENERATIVE ADVERSARIAL NETS

superhans

Mastering sketching: Adversarial augmentation for structured prediction

Danbooru2017: A large-scale crowd-sourced and tagged anime

Make.girls.moe

Learning to simplify: Fully convolutional networks for rough sketch cleanup

nagadomi

Field of Study

Journal Information

Name

Page

Venue Information

Name

Type

URL

Alternate Names