{"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/layoutvae-stochastic-scene-layout-generation","title":"LayoutVAE: Stochastic Scene Layout Generation From a Label Set","arxiv_id":"1907.10719","date":"2019-07-24","proceeding":"ICCV 2019 10","authors":["Akash Abdu Jyothi","Thibaut Durand","JiaWei He","Leonid Sigal","Greg Mori"],"abstract":"Recently there is an increasing interest in scene generation within the research community. However, models used for generating scene layouts from textual description largely ignore plausible visual variations within the structure dictated by the text. We propose LayoutVAE, a variational autoencoder based framework for generating stochastic scene layouts. LayoutVAE is a versatile modeling framework that allows for generating full image layouts given a label set, or per label layouts for an existing image given a new label. In addition, it is also capable of detecting unusual layouts, potentially providing a way to evaluate layout generation problem. Extensive experiments on MNIST-Layouts and challenging COCO 2017 Panoptic dataset verifies the effectiveness of our proposed framework.","url_abs":"https://arxiv.org/abs/1907.10719v3","url_pdf":"https://arxiv.org/pdf/1907.10719v3.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":"layoutvae-stochastic-scene-layout-generation","repo_url":"https://github.com/kampta/DeepLayout","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"layoutvae-stochastic-scene-layout-generation","repo_url":"https://github.com/Layout-Generation/layout-generation/tree/master/LayoutVAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"layout-generation","task_name":"Layout Generation"},{"task_slug":"scene-generation","task_name":"Scene Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1907.10719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.10719"}},"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/Layout-Generation/layout-generation/tree/master/LayoutVAE","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/kampta/DeepLayout","reach":null}],"summary":{"ran_honours":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"listed":{"samples":3,"ran":2,"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":"ca5a0a36580f7ced","entry":"gen_colors","repo":"kampta/DeepLayout","repo_kind":"listed","path":"layout_vae/train_layouts.py","file_url":"https://github.com/kampta/DeepLayout/blob/HEAD/layout_vae/train_layouts.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ca5a0a36580f7ced"}},{"code_sha256_prefix":"9a013752bc8144e3","entry":"plot_layout","repo":"kampta/DeepLayout","repo_kind":"listed","path":"layout_vae/train_layouts.py","file_url":"https://github.com/kampta/DeepLayout/blob/HEAD/layout_vae/train_layouts.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"9a013752bc8144e3"}},{"code_sha256_prefix":"cd7868cef4499a46","entry":"evaluate","repo":"kampta/DeepLayout","repo_kind":"listed","path":"layout_vae/train_counts.py","file_url":"https://github.com/kampta/DeepLayout/blob/HEAD/layout_vae/train_counts.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"cd7868cef4499a46"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}