Sketch or line art colorization is a research field with significant market demand. Different from photo colorization which strongly relies on texture information, sketch colorization is more challenging as sketches may not have texture. Even worse, color, texture, and gradient have to be generated from the abstract sketch lines. In this paper, we propose a semi-automatic learning-based framework to colorize sketches with proper color, texture as well as gradient. Our framework consists of two stages. In the first drafting stage, our model guesses color regions and splashes a rich variety of colors over the sketch to obtain a color draft. In the second refinement stage, it detects the unnatural colors and artifacts, and try to fix and refine the result. Comparing to existing approaches, this two-stage design effectively divides the complex colorization task into two simpler and goal-clearer subtasks. This eases the learning and raises the quality of colorization. Our model resolves the artifacts such as water-color blurring, color distortion, and dull textures. We build an interactive software based on our model for evaluation. Users can iteratively edit and refine the colorization. We evaluate our learning model and the interactive system through an extensive user study. Statistics shows that our method outperforms the state-of-art techniques and industrial applications in several aspects including, the visual quality, the ability of user control, user experience, and other metrics.

Figure 1: Examples of combination results on sketch images (top-left) and style images (bottom-left). Our approach automatically applies the semantic features of an existing painting to an unfinished sketch. Our network has learned to classify the hair, eyes, skin and clothes, and has the ability to paint these features according to a sketch. More results can be seen at the end of paper. AbstractRecently, with the revolutionary neural style transferring methods [1,3,4,8,17], creditable paintings can be synthesized automatically from content images and style images. However, when it comes to the task of applying a painting's style to a anime sketch, these methods will just randomly colorize sketch lines as outputs ( fig. 7) and fail in the main task: specific style tranfer. In this paper, we integrated residual U-net to apply the style to the grayscale sketch with auxiliary classifier generative adversarial network (AC-GAN) [12]. The whole process is automatic and fast, and the results are creditable in the quality of art style as well as colorization.

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