Interactive Style Transfer: All is Your Palette
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Abstract (arXiv)
Neural style transfer (NST) can create impressive artworks by transferring reference style to content image. Current image-to-image NST methods are short of fine-grained controls, which are often demanded by artistic editing. To mitigate this limitation, we propose a drawing-like interactive style transfer (IST) method, by which users can interactively create a harmonious-style image. Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image. To determine the action scope, we formulate a fluid simulation algorithm, which takes styles as pigments around the position of brush interaction, and diffusion in style or content images according to the similarity maps. Our IST method expands the creative dimension of NST. By dipping and painting, even employing one style image can produce thousands of eye-catching works. The demo video is available in supplementary files or in http://mmcheng.net/ist.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 25 Mar 2022.
Builds on
28 of 49 references.
References · 28
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16 more
- 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
- 2016Controlling Perceptual Factors in Neural Style Transfercited by 99
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- 2021Rethinking Style Transfer: From Pixels to Parameterized Brushstrokescited by 42
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- 2017Laplacian-Steered Neural Style Transfercited by 17
- 2020Fast Soft Color Segmentationcited by 13
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- 2021Interactive Multi-level Stroke Control for Neural Style Transfercited by 1
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