Line Art Correlation Matching Network for Automatic Animation Colorization

Automatic animation line art colorization is a challenging computer vision problem since line art is a highly sparse and abstracted information and there exists a strict requirement for the color and style consistency between frames. Recently, a lot of GAN(Generative Adversarial Network) based image-to-image transfer method for single line art colorization has emerged. They can generate perceptually appealing result conditioned on line art. However,these methods can not be adopted to the task of animation colorization because of the lack of consideration of in-between frame consistency. Existing methods simply input the previous colored frame as a reference to color the next line art, which will mislead the colorization due to the spatial misalignment of the previous colored frame and the next line art especially at positions where apparent changes happen. To address these challenges, we design a kind of matching model called CM(co-rrelation matching) to align the colored reference in an learnable way and integrate the model into an U-Net structure generator in a coarse-to-fine manner. Extension evaluations shows that CM model can effectively improve the in-between consistency and generating quality expecially when the motion is intense and diverse.