{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semantic-style-transfer-and-turning-two-bit","title":"Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks","arxiv_id":"1603.01768","date":"2016-03-05","proceeding":null,"authors":["Alex J. Champandard"],"abstract":"Convolutional neural networks (CNNs) have proven highly effective at image\nsynthesis and style transfer. For most users, however, using them as tools can\nbe a challenging task due to their unpredictable behavior that goes against\ncommon intuitions. This paper introduces a novel concept to augment such\ngenerative architectures with semantic annotations, either by manually\nauthoring pixel labels or using existing solutions for semantic segmentation.\nThe result is a content-aware generative algorithm that offers meaningful\ncontrol over the outcome. Thus, we increase the quality of images generated by\navoiding common glitches, make the results look significantly more plausible,\nand extend the functional range of these algorithms---whether for portraits or\nlandscapes, etc. Applications include semantic style transfer and turning\ndoodles with few colors into masterful paintings!","url_abs":"http://arxiv.org/abs/1603.01768v1","url_pdf":"http://arxiv.org/pdf/1603.01768v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/Garfield35/Doodle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/endywon/texture-reformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/factoryIO/1-simple_neural_style_transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/innat/ML-Bookmarks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/innat/ML-Resource","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/jia-yi-chen/Illumination-guided-Neural-Style-Transfer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"semantic-style-transfer-and-turning-two-bit","repo_url":"https://github.com/paulwarkentin/pytorch-neural-doodle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.01768","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}