computer-vision-1

Line Art Colorization

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

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

Zhihui Wang, Haojie Li, Yuanzheng Ci, Xinzhu Ma, Zhongxuan Luo•Wed Aug 08 2018

This work proposes a novel deep conditional adversarial architecture for scribble based anime line art colorization that integrates the conditional framework with WGAN-GP criteria as well as the perceptual loss to enable it to robustly train a deep network that makes the synthesized images more natural and real.

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Eliminating Gradient Conflict in Reference-based Line-Art Colorization

Zhao Kang, Zhengyang Geng, Wenyu Chen, Yibo Yang•Tue Jul 12 2022

This work proposes a novel attention mechanism using this training strategy, Stop-Gradient Attention (SGA), outperforming the attention baseline by a large margin with better training stability and demonstrates significant improvements in Fr\'echet Inception Distance and structural similarity index measure on several benchmarks.

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Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss

Ho Young Jhoo, Hyunsu Kim, Eunhyeok Park, Sungjoo Yoo•Thu Aug 15 2019

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.

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Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models

E. Simo-Serra, Hernan Carrillo, Michaël Clément, Aurélie Bugeau•Wed May 31 2023

Colorization of line art drawings is an important task in illustration and animation workflows. However, this highly laborious process is mainly done manually, limiting the creative productivity. This paper presents a novel interactive approach for line art colorization using conditional Diffusion Probabilistic Models (DPMs). In our proposed approach, the user provides initial color strokes for colorizing the line art. The strokes are then integrated into the conditional DPM-based colorization process by means of a coupled implicit and explicit conditioning strategy to generates diverse and high-quality colorized images. We evaluate our proposal and show it outperforms existing state-of-the-art approaches using the FID, LPIPS and SSIM metrics.

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