LVCD: Reference-based Lineart Video Colorization with Diffusion Models
Authors: name: Zhitong Huang, name: Mohan Zhang, name: Jing Liao

We propose the first video diffusion framework for reference-based lineart
video colorization. Unlike previous works that rely solely on image generative
models to colorize lineart frame by frame, our approach leverages a large-scale
pretrained video diffusion model to generate colorized animation videos. This
approach leads to more temporally consistent results and is better equipped to
handle large motions. Firstly, we introduce Sketch-guided ControlNet which
provides additional control to finetune an image-to-video diffusion model for
controllable video synthesis, enabling the generation of animation videos
conditioned on lineart. We then propose Reference Attention to facilitate the
transfer of colors from the reference frame to other frames containing fast and
expansive motions. Finally, we present a novel scheme for sequential sampling,
incorporating the Overlapped Blending Module and Prev-Reference Attention, to
extend the video diffusion model beyond its original fixed-length limitation
for long video colorization. Both qualitative and quantitative results
demonstrate that our method significantly outperforms state-of-the-art
techniques in terms of frame and video quality, as well as temporal
consistency. Moreover, our method is capable of generating high-quality, long
temporal-consistent animation videos with large motions, which is not
achievable in previous works. Our code and model are available at
https://luckyhzt.github.io/lvcd.