Artist-Guided Semiautomatic Animation Colorization

TL;DR

This paper introduces a semi-automatic animation colorization method that combines artist input with an adversarial framework to efficiently produce temporally consistent colored frames, reducing workload while preserving artistic intent.

Contribution

It presents a novel artist-guided, adversarial image-to-image approach that incorporates color hints and temporal data for improved animation colorization.

Findings

Reduces coloring workload for animation studios.

Maintains artistic authenticity with minimal artist input.

Produces temporally consistent colored frames.

Abstract

There is a delicate balance between automating repetitive work in creative domains while staying true to an artist's vision. The animation industry regularly outsources large animation workloads to foreign countries where labor is inexpensive and long hours are common. Automating part of this process can be incredibly useful for reducing costs and creating manageable workloads for major animation studios and outsourced artists. We present a method for automating line art colorization by keeping artists in the loop to successfully reduce this workload while staying true to an artist's vision. By incorporating color hints and temporal information to an adversarial image-to-image framework, we show that it is possible to meet the balance between automation and authenticity through artist's input to generate colored frames with temporal consistency.