  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
