Automatic Temporally Coherent Video Colorization
This work addresses the need for cost reduction and elimination of redundant in-between frame colorization in the anime industry, though it is incremental as it builds on existing methods.
The paper tackles the problem of colorizing anime line art frames for video production, achieving temporally consistent video sequences by enhancing existing image-to-image translation methods with an additional condition in the generator and discriminator.
Greyscale image colorization for applications in image restoration has seen significant improvements in recent years. Many of these techniques that use learning-based methods struggle to effectively colorize sparse inputs. With the consistent growth of the anime industry, the ability to colorize sparse input such as line art can reduce significant cost and redundant work for production studios by eliminating the in-between frame colorization process. Simply using existing methods yields inconsistent colors between related frames resulting in a flicker effect in the final video. In order to successfully automate key areas of large-scale anime production, the colorization of line arts must be temporally consistent between frames. This paper proposes a method to colorize line art frames in an adversarial setting, to create temporally coherent video of large anime by improving existing image to image translation methods. We show that by adding an extra condition to the generator and discriminator, we can effectively create temporally consistent video sequences from anime line arts. Code and models available at: https://github.com/Harry-Thasarathan/TCVC