{"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/human-centric-indoor-scene-synthesis-using","title":"Human-centric Indoor Scene Synthesis Using Stochastic Grammar","arxiv_id":"1808.08473","date":"2018-08-25","proceeding":"CVPR 2018 6","authors":["Siyuan Qi","Yixin Zhu","Siyuan Huang","Chenfanfu Jiang","Song-Chun Zhu"],"abstract":"We present a human-centric method to sample and synthesize 3D room layouts\nand 2D images thereof, to obtain large-scale 2D/3D image data with perfect\nper-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed\nto represent indoor scenes. The S-AOG is a probabilistic grammar model, in\nwhich the terminal nodes are object entities. Human contexts as contextual\nrelations are encoded by Markov Random Fields (MRF) on the terminal nodes. We\nlearn the distributions from an indoor scene dataset and sample new layouts\nusing Monte Carlo Markov Chain. Experiments demonstrate that our method can\nrobustly sample a large variety of realistic room layouts based on three\ncriteria: (i) visual realism comparing to a state-of-the-art room arrangement\nmethod, (ii) accuracy of the affordance maps with respect to groundtruth, and\n(ii) the functionality and naturalness of synthesized rooms evaluated by human\nsubjects. The code is available at\nhttps://github.com/SiyuanQi/human-centric-scene-synthesis.","url_abs":"http://arxiv.org/abs/1808.08473v1","url_pdf":"http://arxiv.org/pdf/1808.08473v1.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":"human-centric-indoor-scene-synthesis-using","repo_url":"https://github.com/SiyuanQi/human-centric-scene-synthesis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"indoor-scene-synthesis","task_name":"Indoor Scene Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.08473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.08473"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/SiyuanQi/human-centric-scene-synthesis","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"5d5053380b78ff19","entry":"set_logger","repo":"SiyuanQi/human-centric-scene-synthesis","repo_kind":"official","path":"src/python/config.py","file_url":"https://github.com/SiyuanQi/human-centric-scene-synthesis/blob/HEAD/src/python/config.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5d5053380b78ff19"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}