Im2Painting: Economical Painterly Stylization
Abstract: This paper presents a new approach to stroke optimization for image stylization based on economical paintings. Given an input image, our method generates a set of strokes to approximate the input in a variety of economical artistic styles. The term economy in this paper refers a particular range of paintings that use few brushstrokes or limited time. Inspired by this, and unlike previous methods where a painting requires a large number of brushstrokes, our method is able to paint economical painting styles with fewer brush strokes, just as a skilled artist can effectively capture the essence of a scene with relatively few brush strokes. Moreover, we show effective results using a much simpler architecture than previous gradient-based methods, avoiding the challenges of training control models. Instead, our method learns a direct non-linear mapping from an image to a collection of strokes. Perhaps surprisingly, this produces higher-precision results than previous methods, in addition to style variations that are fast to train on a single GPU.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: Added paragraphs 4 and 5 in Introduction, and modified summary bullets.
Rearranged section 4, and added independent section 5 for evaluation.
Modified text in section 5.
Modified text in section 7, Conclusions.
Modified figures 5 and 6 for better readability
Added section 9 and 10 in appendix.
Addressed minor changes per requested by reviewers.
Assigned Action Editor: ~Jiajun_Wu1
Submission Number: 173
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