{"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/fast-and-flexible-indoor-scene-synthesis-via","title":"Fast and Flexible Indoor Scene Synthesis via Deep Convolutional Generative Models","arxiv_id":"1811.12463","date":"2018-11-29","proceeding":"CVPR 2019 6","authors":["Daniel Ritchie","Kai Wang","Yu-an Lin"],"abstract":"We present a new, fast and flexible pipeline for indoor scene synthesis that\nis based on deep convolutional generative models. Our method operates on a\ntop-down image-based representation, and inserts objects iteratively into the\nscene by predicting their category, location, orientation and size with\nseparate neural network modules. Our pipeline naturally supports automatic\ncompletion of partial scenes, as well as synthesis of complete scenes. Our\nmethod is significantly faster than the previous image-based method and\ngenerates result that outperforms it and other state-of-the-art deep generative\nscene models in terms of faithfulness to training data and perceived visual\nquality.","url_abs":"http://arxiv.org/abs/1811.12463v1","url_pdf":"http://arxiv.org/pdf/1811.12463v1.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":"fast-and-flexible-indoor-scene-synthesis-via","repo_url":"https://github.com/nv-tlabs/atiss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"fast-and-flexible-indoor-scene-synthesis-via","repo_url":"https://github.com/tangjiapeng/diffuscene","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"indoor-scene-synthesis","task_name":"Indoor Scene Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.12463","atlas_url":"https://app.syntology.ai/?focus=1811.12463","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}