Deformable Style Transfer

March 24, 2020 ยท Entered Twilight ยท ๐Ÿ› European Conference on Computer Vision

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Repo contents: NBB, README.md, cleanpoints.py, demo_DST.ipynb, demo_warp.ipynb, dst.bib, example, job_example.sh, loss.py, main.py, styletransfer.py, utils_keypoints.py, utils_misc.py, utils_plot.py, utils_pyr.py, utils_save.py, vggfeatures.py, warp.py

Authors Sunnie S. Y. Kim, Nicholas Kolkin, Jason Salavon, Gregory Shakhnarovich arXiv ID 2003.11038 Category cs.CV: Computer Vision Cross-listed cs.GR, cs.LG Citations 63 Venue European Conference on Computer Vision Repository https://github.com/sunniesuhyoung/DST โญ 266 Last Checked 1 month ago
Abstract
Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image. Unlike previous geometry-aware stylization methods, our approach is neither restricted to a particular domain (such as human faces), nor does it require training sets of matching style/content pairs. We demonstrate our method on a diverse set of content and style images including portraits, animals, objects, scenes, and paintings. Code has been made publicly available at https://github.com/sunniesuhyoung/DST.
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