For realistic and vivid colorization, generative priors have recently been
exploited. However, such generative priors often fail for in-the-wild complex
images due to their limited representation space. In this paper, we propose
BigColor, a novel colorization approach that provides vivid colorization for
diverse in-the-wild images with complex structures. While previous generative
priors are trained to synthesize both image structures and colors, we learn a
generative color prior to focus on color synthesis given the spatial structure
of an image. In this way, we reduce the burden of synthesizing image structures
from the generative prior and expand its representation space to cover diverse
images. To this end, we propose a BigGAN-inspired encoder-generator network
that uses a spatial feature map instead of a spatially-flattened BigGAN latent
code, resulting in an enlarged representation space. Our method enables robust
colorization for diverse inputs in a single forward pass, supports arbitrary
input resolutions, and provides multi-modal colorization results. We
demonstrate that BigColor significantly outperforms existing methods especially
on in-the-wild images with complex structures.

本研究提出了一种名为 BigColor 的彩色图像上色方法，通过学习生成色彩先验来减轻以前的生成先验对图像结构和颜色的合成负担，并使用一种基于 encoder-generator 网络的方法来扩展其表示空间以覆盖各种野外图像结构，提供了具有极强表现力和多模彩色方案的彩色图像上色模型。