{"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/texture-synthesis-with-recurrent-variational","title":"Texture Synthesis with Recurrent Variational Auto-Encoder","arxiv_id":"1712.08838","date":"2017-12-23","proceeding":null,"authors":["Rohan Chandra","Sachin Grover","Kyungjun Lee","Moustafa Meshry","Ahmed Taha"],"abstract":"We propose a recurrent variational auto-encoder for texture synthesis. A\nnovel loss function, FLTBNK, is used for training the texture synthesizer. It\nis rotational and partially color invariant loss function. Unlike L2 loss,\nFLTBNK explicitly models the correlation of color intensity between pixels. Our\ntexture synthesizer generates neighboring tiles to expand a sample texture and\nis evaluated using various texture patterns from Describable Textures Dataset\n(DTD). We perform both quantitative and qualitative experiments with various\nloss functions to evaluate the performance of our proposed loss function\n(FLTBNK) --- a mini-human subject study is used for the qualitative evaluation.","url_abs":"http://arxiv.org/abs/1712.08838v1","url_pdf":"http://arxiv.org/pdf/1712.08838v1.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":"texture-synthesis-with-recurrent-variational","repo_url":"https://github.com/MoustafaMeshry/draw","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"texture-synthesis","task_name":"Texture Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}