inkn'hue: Enhancing Manga Colorization from Multiple Priors with Alignment Multi-Encoder VAE

TL;DR

This paper introduces inkn'hue, a novel manga colorization framework that uses a multi-encoder VAE to improve colorization quality by aligning shading and vibrant coloring, allowing reference images and manual hints.

Contribution

The paper presents a specialized manga colorization method employing a multi-encoder VAE for better alignment and colorization, addressing limitations of prior single-step or manual approaches.

Findings

Achieves clearer and more vibrant manga colorization results.

Supports reference images and manual hints for customization.

Outperforms existing methods in colorization quality.

Abstract

Manga, a form of Japanese comics and distinct visual storytelling, has captivated readers worldwide. Traditionally presented in black and white, manga's appeal lies in its ability to convey complex narratives and emotions through intricate line art and shading. Yet, the desire to experience manga in vibrant colors has sparked the pursuit of manga colorization, a task of paramount significance for artists. However, existing methods, originally designed for line art and sketches, face challenges when applied to manga. These methods often fall short in achieving the desired results, leading to the need for specialized manga-specific solutions. Existing approaches frequently rely on a single training step or extensive manual artist intervention, which can yield less satisfactory outcomes. To address these challenges, we propose a specialized framework for manga colorization. Leveraging established models for shading and vibrant coloring, our approach aligns both using a multi-encoder VAE. This structured workflow ensures clear and colorful results, with the option to incorporate reference images and manual hints.