{"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/image-synthesis-from-reconfigurable-layout","title":"Image Synthesis From Reconfigurable Layout and Style","arxiv_id":"1908.07500","date":"2019-08-20","proceeding":"ICCV 2019 10","authors":["Wei Sun","Tianfu Wu"],"abstract":"Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i.e., bounding boxes + class labels in an image lattice) and style (i.e., structural and appearance variations encoded by latent vectors), especially at high resolution. By reconfigurable, it means that a model can preserve the intrinsic one-to-many mapping from a given layout to multiple plausible images with different styles, and is adaptive with respect to perturbations of a layout and style latent code. In this paper, we present a layout- and style-based architecture for generative adversarial networks (termed LostGANs) that can be trained end-to-end to generate images from reconfigurable layout and style. Inspired by the vanilla StyleGAN, the proposed LostGAN consists of two new components: (i) learning fine-grained mask maps in a weakly-supervised manner to bridge the gap between layouts and images, and (ii) learning object instance-specific layout-aware feature normalization (ISLA-Norm) in the generator to realize multi-object style generation. In experiments, the proposed method is tested on the COCO-Stuff dataset and the Visual Genome dataset with state-of-the-art performance obtained. The code and pretrained models are available at \\url{https://github.com/iVMCL/LostGANs}.","url_abs":"https://arxiv.org/abs/1908.07500v1","url_pdf":"https://arxiv.org/pdf/1908.07500v1.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":"image-synthesis-from-reconfigurable-layout","repo_url":"https://github.com/iVMCL/LostGANs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"image-synthesis-from-reconfigurable-layout","repo_url":"https://github.com/WillSuen/LostGANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"image-synthesis-from-reconfigurable-layout","repo_url":"https://github.com/kroniidvul/LostGANs-mnist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"image-synthesis-from-reconfigurable-layout","repo_url":"https://github.com/stanifrolov/attrlostgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"layout-to-image-generation","task_name":"Layout-to-Image Generation"}],"methods":[{"method_slug":"adaptive-instance-normalization","method_name":"Adaptive Instance Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"r1-regularization","method_name":"R1 Regularization"},{"method_slug":"stylegan","method_name":"StyleGAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/layout-to-image-generation-on-coco-stuff-3","task":"Layout-to-Image Generation","dataset":"COCO-Stuff 128x128","model":"LostGAN","rank_in_archive_order":3,"of":5,"metrics":{"FID":"29.65","Inception Score":"13.8","SceneFID":"20.03"},"uses_additional_data":false},{"leaderboard":"/sota/layout-to-image-generation-on-coco-stuff-2","task":"Layout-to-Image Generation","dataset":"COCO-Stuff 64x64","model":"LostGAN","rank_in_archive_order":2,"of":5,"metrics":{"FID":"34.31","Inception Score":"9.8"},"uses_additional_data":false},{"leaderboard":"/sota/layout-to-image-generation-on-visual-genome-3","task":"Layout-to-Image Generation","dataset":"Visual Genome 128x128","model":"LostGAN","rank_in_archive_order":5,"of":5,"metrics":{"FID":"29.36","Inception Score":"11.1","SceneFID":"13.17"},"uses_additional_data":false},{"leaderboard":"/sota/layout-to-image-generation-on-visual-genome-2","task":"Layout-to-Image Generation","dataset":"Visual Genome 64x64","model":"LostGAN","rank_in_archive_order":3,"of":4,"metrics":{"FID":"34.75","Inception Score":"8.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1908.07500","atlas_url":"https://app.syntology.ai/?focus=1908.07500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.07500"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. 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