We detail the challenges in achieving this dataset and present a human-in-the-loop workflow namely Feasibility-based Assignment Recommendation (FAR) to enable large-scale annotating.
We show that partially ""erasing"" the appearance preservation facilitate adequate image smoothing.
We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i. e., the areas where the user scribbles should propagate and influence.
To this end, we create a large-scale dataset with these three components annotated by artists in a human-in-the-loop manner.
Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images.