{"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/interactive-image-synthesis-with-panoptic","title":"Interactive Image Synthesis with Panoptic Layout Generation","arxiv_id":"2203.02104","date":"2022-03-04","proceeding":"CVPR 2022 1","authors":["Bo wang","Tao Wu","Minfeng Zhu","Peng Du"],"abstract":"Interactive image synthesis from user-guided input is a challenging task when users wish to control the scene structure of a generated image with ease.Although remarkable progress has been made on layout-based image synthesis approaches, in order to get realistic fake image in interactive scene, existing methods require high-precision inputs, which probably need adjustment several times and are unfriendly to novice users. When placement of bounding boxes is subject to perturbation, layout-based models suffer from \"missing regions\" in the constructed semantic layouts and hence undesirable artifacts in the generated images. In this work, we propose Panoptic Layout Generative Adversarial Networks (PLGAN) to address this challenge. The PLGAN employs panoptic theory which distinguishes object categories between \"stuff\" with amorphous boundaries and \"things\" with well-defined shapes, such that stuff and instance layouts are constructed through separate branches and later fused into panoptic layouts. In particular, the stuff layouts can take amorphous shapes and fill up the missing regions left out by the instance layouts. We experimentally compare our PLGAN with state-of-the-art layout-based models on the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN are not only visually demonstrated but quantitatively verified in terms of inception score, Fr\\'echet inception distance, classification accuracy score, and coverage.","url_abs":"https://arxiv.org/abs/2203.02104v3","url_pdf":"https://arxiv.org/pdf/2203.02104v3.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":"interactive-image-synthesis-with-panoptic","repo_url":"https://github.com/wb-finalking/PLGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"layout-generation","task_name":"Layout Generation"},{"task_slug":"layout-to-image-generation","task_name":"Layout-to-Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/layout-to-image-generation-on-visual-genome-3","task":"Layout-to-Image Generation","dataset":"Visual Genome 128x128","model":"PLGAN","rank_in_archive_order":2,"of":5,"metrics":{"FID":"20.62","Inception Score":"10.6"},"uses_additional_data":false},{"leaderboard":"/sota/layout-to-image-generation-on-visual-genome-4","task":"Layout-to-Image Generation","dataset":"Visual Genome 256x256","model":"PLGAN","rank_in_archive_order":2,"of":4,"metrics":{"FID":"28.06","Inception Score":"13.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.02104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02104"}},"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/wb-finalking/PLGAN","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/wbfinalking/PLGAN","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"summary":{"unverified":11},"by_repo_kind":{"official":{"samples":11,"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 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