{"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/awesome-typography-statistics-based-text","title":"Awesome Typography: Statistics-Based Text Effects Transfer","arxiv_id":"1611.09026","date":"2016-11-28","proceeding":"CVPR 2017 7","authors":["Shuai Yang","Jiaying Liu","Zhouhui Lian","Zongming Guo"],"abstract":"In this work, we explore the problem of generating fantastic special-effects\nfor the typography. It is quite challenging due to the model diversities to\nillustrate varied text effects for different characters. To address this issue,\nour key idea is to exploit the analytics on the high regularity of the spatial\ndistribution for text effects to guide the synthesis process. Specifically, we\ncharacterize the stylized patches by their normalized positions and the optimal\nscales to depict their style elements. Our method first estimates these two\nfeatures and derives their correlation statistically. They are then converted\ninto soft constraints for texture transfer to accomplish adaptive multi-scale\ntexture synthesis and to make style element distribution uniform. It allows our\nalgorithm to produce artistic typography that fits for both local texture\npatterns and the global spatial distribution in the example. Experimental\nresults demonstrate the superiority of our method for various text effects over\nconventional style transfer methods. In addition, we validate the effectiveness\nof our algorithm with extensive artistic typography library generation.","url_abs":"http://arxiv.org/abs/1611.09026v2","url_pdf":"http://arxiv.org/pdf/1611.09026v2.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":"awesome-typography-statistics-based-text","repo_url":"https://github.com/ycjing/Character-Stylization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"text-effects-transfer","task_name":"Text Effects Transfer"},{"task_slug":"texture-synthesis","task_name":"Texture Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1611.09026","atlas_url":"https://app.syntology.ai/?focus=1611.09026","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}