{"id":6916,"url":"https://github.com/bcmi/Awesome-Generative-Image-Composition","name":"Awesome-Generative-Image-Composition","description":"A curated list of papers, code, and resources pertaining to generative image composition or object insertion. ","projects_count":54,"last_synced_at":"2026-08-09T11:00:27.194Z","repository":{"id":183332532,"uuid":"669957067","full_name":"bcmi/Awesome-Generative-Image-Composition","owner":"bcmi","description":"A curated list of papers, code, and resources pertaining to generative image composition or object insertion. ","archived":false,"fork":false,"pushed_at":"2026-06-03T04:01:53.000Z","size":3202,"stargazers_count":153,"open_issues_count":0,"forks_count":7,"subscribers_count":9,"default_branch":"main","last_synced_at":"2026-07-21T03:04:20.609Z","etag":null,"topics":["diffusion-model","image-composition","object-insertion"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bcmi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-07-24T01:10:09.000Z","updated_at":"2026-07-12T07:38:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"a0e1d93f-60a1-471b-9414-49ba2c87ef1c","html_url":"https://github.com/bcmi/Awesome-Generative-Image-Composition","commit_stats":null,"previous_names":["bcmi/awesome-generative-image-composition"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/bcmi/Awesome-Generative-Image-Composition","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bcmi%2FAwesome-Generative-Image-Composition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bcmi%2FAwesome-Generative-Image-Composition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bcmi%2FAwesome-Generative-Image-Composition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bcmi%2FAwesome-Generative-Image-Composition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bcmi","download_url":"https://codeload.github.com/bcmi/Awesome-Generative-Image-Composition/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bcmi%2FAwesome-Generative-Image-Composition/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36450650,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-06T04:43:03.162Z","status":"online","status_checked_at":"2026-08-09T02:00:06.528Z","response_time":130,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-07T21:30:13.579Z","updated_at":"2026-08-09T11:00:27.194Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Papers","Survey","Related Topics","Leaderboard","Foreground: image;  Background: image","Datasets","Evaluation Metrics","Other Resources","Online Demo"],"sub_categories":["Training-based","Evaluating Your Results","Object-Guided","Training-free","(Object+Text)-Guided"],"readme":"\n# Awesome Generative Image Composition [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated list of resources including papers, datasets, and relevant links pertaining to generative image composition (object insertion). **Generative image composition aims to generate plausible composite images based on a background image (optional bounding box) and a (*resp.*, a few) foreground image (*resp.*, images) of a specific object.** For more complete resources on general image composition ([object insertion](https://github.com/bcmi/Awesome-Object-Insertion)), please refer to [Awesome-Image-Composition](https://github.com/bcmi/Awesome-Object-Insertion).\n\n\u003cp align='center'\u003e  \n  \u003cimg src='./figures/task.jpg'  width=90% /\u003e\n\u003c/p\u003e\n\n## Contributing\n\nContributions are welcome.  If you wish to contribute, feel free to send a pull request. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a pull request.\n\n## Table of Contents\n+ [Survey](#Survey)\n+ [Online Demo](#Online-demo)\n+ [Evaluation Metrics](#Evaluation-metrics)\n+ [Datasets](#Datasets)\n+ [Papers](#Papers)\n+ [Other Resources](#Other-resources)\n\n## Survey\n\nA brief review on generative image composition is included in the following survey on image composition:\n\nLi Niu, Wenyan Cong, Liu Liu, Yan Hong, Bo Zhang, Jing Liang, Liqing Zhang: \"*Making Images Real Again: A Comprehensive Survey on Deep Image Composition.*\" arXiv:2106.14490 (2021). [[arxiv]](https://arxiv.org/pdf/2106.14490.pdf)  [[slides]](https://www.ustcnewly.com/download/Image_composition_tutorial.pdf)\n\n## Online Demo\n\nTry this [online demo](http://libcom.ustcnewly.com/) for image composition (object insertion) built upon [libcom](https://github.com/bcmi/libcom) toolbox and have fun!\n\n[![]](https://github.com/user-attachments/assets/87416ec5-2461-42cb-9f2d-5030b1e1b5ec)\n\n## Evaluation Metrics\n\n+ [Composite-Image-Evaluation](https://github.com/bcmi/Composite-Image-Evaluation)\n\n\n## Datasets\n+ [MureCom](https://github.com/bcmi/Image-Composition-Dataset-MureCom) (within-domain, multi-ref): This dataset contains 32 category subfolders. Each category subfolder has: (1) 20 background images with bounding boxes to insert foreground object; (2) 3 foreground sets (5 images each) with object masks, bounding box masks, object-free variants, and lighting variants.\n+ [COCOEE](https://github.com/Fantasy-Studio/Paint-by-Example?tab=readme-ov-file#test-benchmark) (within-domain, single-ref): 500 background images from MSCOCO validation set.  Each background image has a bounding box and a foreground image from MSCOCO training set.\n+ [TF-ICON test benchmark](https://github.com/Shilin-LU/TF-ICON?tab=readme-ov-file#tf-icon-test-benchmark) (cross-domain, single-ref): 332 samples. Each sample consists of a background image, a foreground image, a user mask, and a text prompt.\n+ [DreamEditBench](https://huggingface.co/datasets/tianleliphoebe/DreamEditBench) (within-domain, multi-ref): 220 background images and 30 unique foreground objects from 15 categories. \n+ [SAM-FB](https://github.com/KaKituken/affordance-aware-any) (within-domain, single-ref): built upon SA-1B (SAM dataset). 3,160,403 images with 3,439 foreground categories.\n+ [Subjects 200K](https://github.com/Yuanshi9815/Subjects200K) (within-domain, double-ref): 200,000 paired images. Each pair has the same subject yet various scene contexts.\n+ [ORIDa](https://hello-jinwoo.github.io/orida/) (within-domain, multi-ref): 200 unique foreground objects. Each object is placed in an average of 50 diverse scenes. In each scene, one object is placed at 1~4 different positions.\n+ [AnyInsertion](https://huggingface.co/datasets/WensongSong/AnyInsertion_V1) (within-domain, single-ref): The training set includes 136,385 samples across two prompt types: 58,188 mask-prompt image pairs and 78,197 text-prompt image pairs. The test set includes 158 data pairs: 120 mask-prompt pairs and 38 text-prompt pairs.\n+ [DreamFuse](https://huggingface.co/datasets/LL3RD/DreamFuse) (within/cross-domain, single-ref): 80k diverse fusion scenarios (foreground, background, fused image). Over half of the dataset features outdoor backgrounds, and approximately 23k images include hand-held scenarios.\n\n\n## Papers\n\n### Training-free\n+ Shilin Lu, Zhuming Lian, Zihan Zhou, Shaocong Zhang, Chen Zhao, Adams Wai-Kin Kong: \"*Does FLUX Already Know How to Perform Physically Plausible Image Composition?*\" ICLR (2026) [[arxiv]](https://arxiv.org/pdf/2509.21278)\n+ Yu Xu, Fan Tang, You Wu, Lin Gao, Oliver Deussen, Hongbin Yan, Jintao Li, Juan Cao, Tong-Yee Lee: \"*In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulation.*\" ACM SIGGRAPH ASIA (2025) [[arxiv]](https://arxiv.org/pdf/2505.20271) [[paper]](https://dl.acm.org/doi/epdf/10.1145/3757377.3763820) [[code]](https://github.com/ICTMCG/In-context-Brush)\n+ Haowen Li, Zhenfeng Fan, Zhang Wen, Zhengzhou Zhu, Yunjin Li: \"*AIComposer: Any Style and Content Image Composition via Feature Integration.*\" (**cross-domain**; **+text**)  ICCV (2025) [[arxiv]](https://arxiv.org/pdf/2507.20721) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper.pdf) [[code]](https://github.com/sherlhw/AIComposer)\n+ Pengzhi Li, Qiang Nie, Ying Chen, Xi Jiang, Kai Wu, Yuhuan Lin, Yong Liu, Jinlong Peng, Chengjie Wang, Feng Zheng: \"*Tuning-Free Image Customization with Image and Text Guidance.*\" (**+text**) ECCV (2024) [[arxiv]](https://arxiv.org/pdf/2403.12658) [[paper]](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09769.pdf) [[code]](https://github.com/zrealli/TIGIC)\n+ Kien T. Pham, Jingye Chen, Qifeng Chen: \"*Tale: Training-free cross-domain image composition via adaptive latent manipulation and energy-guided optimization.*\" (**cross-domain**) ACM MM (2024) [[arxiv]](https://arxiv.org/pdf/2408.03637) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3664647.3681079) [[code]](https://github.com/tkpham3105/TALE)\n+ Yibin Wang, Weizhong Zhang, Jianwei Zheng, Cheng Jin: \"*PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering.*\" (**cross-domain**; **+text**) ACM MM (2024) [[arxiv]](https://arxiv.org/pdf/2403.05053) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3664647.3680848) [[code]](https://github.com/CodeGoat24/PrimeComposer)\n+ Shilin Lu, Yanzhu Liu, Adams Wai-Kin Kong: \"*TF-ICON: Diffusion-based Training-free Cross-domain Image Composition.*\" (**cross-domain**; **+text**) ICCV (2023) [[arxiv]](https://arxiv.org/pdf/2403.05053) [[paper]](https://openaccess.thecvf.com/content/ICCV2023/papers/Lu_TF-ICON_Diffusion-Based_Training-Free_Cross-Domain_Image_Composition_ICCV_2023_paper.pdf) [[code]](https://github.com/Shilin-LU/TF-ICON)\n+ Roy Hachnochi, Mingrui Zhao, Nadav Orzech, Rinon Gal, Ali Mahdavi-Amiri, Daniel Cohen-Or, Amit Haim Bermano: \"*Cross-domain Compositing with Pretrained Diffusion Models.*\" (**cross-domain**)  arXiv:2302.10167 (2023) [[arxiv]](https://arxiv.org/pdf/2302.10167.pdf) [[code]](https://github.com/roy-hachnochi/cross-domain-compositing)\n\n### Training-based\n\n####  Free from object-centric finetuning\n+ Jingyuan Wang, Li Niu: \"OSInsert: Towards High-authenticity and High-fidelity Image Composition.\" arXiv:2602.19523 (2026) [[arxiv]](https://arxiv.org/pdf/2602.19523) [[code]](https://github.com/bcmi/OSInsert-Image-Composition)\n+ Wensong Song, Hong Jiang, Zongxing Yang, Ruijie Quan, Yi Yang: \"*Insert Anything: Image Insertion via In-Context Editing in DiT.*\" AAAI (2026) [[arxiv]](https://arxiv.org/pdf/2504.15009) [[code]](https://github.com/song-wensong/insert-anything)\n+ Raghu Vamsi Chittersu, Yuvraj Singh Rathore, Pranav Adlinge, Kunal Swami: \"*Insert In Style: A Zero-Shot Generative Framework for Harmonious Cross-Domain Object Composition.*\" arXiv:2511.15197 (2025) [[arxiv]](https://arxiv.org/pdf/2511.15197)\n+ Dong Liang, Jinyuan Jia, Yuhao Liu, Rynson W.H. Lau: \"*HOComp: Interaction-Aware Human-Object Composition.*\" NeurIPS (2025)  [[arxiv]](https://arxiv.org/pdf/2507.16813) [[paper]](https://openreview.net/pdf/c6b791fd625ad535761f9071136be59309a1bde2.pdf) [[code]](https://github.com/dliang293/HOComp)\n+ Qi Zhang, Guanyu Xing, Mengting Luo, Jianwei Zhang, Yanli Liu: \"*Inserting Objects into Any Background Images via Implicit Parametric Representation.*\" IEEE Transactions on Visualization and Computer Graphics (2025) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10753453)\n+ Lu Yang, Yuanhao Wang, Yicheng Liu, Enze Wang, Ziyang Zhao, Yanqi He, Zexian Song, Hao Lua: \"*UNICOM: Unified, foreground-aware, and context-realistic deep image composition with diffusion model.*\" Neurocomputing (2025) [[paper]](https://www.sciencedirect.com/science/article/pii/S0925231225018016)\n+ Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, Hengshuang Zhao: \"*AnyDoor: Zero-shot Image Customization with Region-to-region Reference.*\"  T-PAMI (2025) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=10976616)\n+ Jinwoo Kim, Sangmin Han, Jinho Jeong, Jiwoo Choi, Dongyeong Kim, Seon Joo Kim: \"*ORIDa: Object-centric Real-world Image Composition Dataset.*\" CVPR (2025) [[arxiv]](https://arxiv.org/pdf/2506.08964) [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ORIDa_Object-centric_Real-world_Image_Composition_Dataset_CVPR_2025_paper.pdf) \n+ Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang, He Zhang, Andrew Gilbert, John Collomosse, Soo Ye Kim: \"*Multitwine: Multi-Object Compositing with Text and Layout Control.*\" (**+text**) CVPR (2025)  [[arxiv]](https://arxiv.org/pdf/2502.05165) [[paper]](https://openaccess.thecvf.com/content/CVPR2025/papers/Tarres_Multitwine_Multi-Object_Compositing_with_Text_and_Layout_Control_CVPR_2025_paper.pdf)\n+ Junjia Huang, Pengxiang Yan, Jiyang Liu, Jie Wu, Zhao Wang, Yitong Wang, Liang Lin, Guanbin Li: \"*DreamFuse: Adaptive Image Fusion with Diffusion Transformer.*\" ICCV (2025) (**+text**) [[arxiv]](https://arxiv.org/pdf/2504.08291) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_DreamFuse_Adaptive_Image_Fusion_with_Diffusion_Transformer_ICCV_2025_paper.pdf) [[code]](https://github.com/LL3RD/DreamFuse-Code) \n+ Haoxuan Wang, Jinlong Peng, Qingdong He, Hao Yang, Ying Jin, Jiafu Wu, Xiaobin Hu, Yanjie Pan, Zhenye Gan, Mingmin Chi, Bo Peng, Yabiao Wang: \"*UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer.*\" ICCV (2025) [[arxiv]](https://arxiv.org/pdf/2503.09277) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_UniCombine_Unified_Multi-Conditional_Combination_with_Diffusion_Transformer_ICCV_2025_paper.pdf) [[code]](https://github.com/Xuan-World/UniCombine)\n+ Daniel Winter, Asaf Shul, Matan Cohen, Dana Berman, Yael Pritch, Alex Rav-Acha, Yedid Hoshen: \"*ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation.*\" ICCV (2025) [[arxiv]](https://arxiv.org/pdf/2412.08645) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Winter_ObjectMate_A_Recurrence_Prior_for_Object_Insertion_and_Subject-Driven_Generation_ICCV_2025_paper.pdf)\n+ Yongsheng Yu, Ziyun Zeng, Haitian Zheng, Jiebo Luo: \"*OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting.*\" ICCV (2025) [[arxiv]](https://arxiv.org/pdf/2503.08677) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_OmniPaint_Mastering_Object-Oriented_Editing_via_Disentangled_Insertion-Removal_Inpainting_ICCV_2025_paper.pdf) [[code]](https://github.com/yeates/OmniPaint)\n+ Zitian Zhang, Frederic Fortier-Chouinard, Mathieu Garon, Anand Bhattad, Jean-Francois Lalonde: \"*ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion.*\" WACV (2025) [[arxiv]](https://arxiv.org/pdf/2410.08168) [[paper]](https://openaccess.thecvf.com/content/WACV2025/papers/Zhang_ZeroComp_Zero-Shot_Object_Compositing_from_Image_Intrinsics_via_Diffusion_WACV_2025_paper.pdf) [[code]](https://github.com/lvsn/ZeroComp)\n+ Jixuan He, Wanhua Li, Ye Liu, Junsik Kim, Donglai Wei, Hanspeter Pfister: \"*Affordance-Aware Object Insertion via Mask-Aware Dual Diffusion.*\" arXiv:2412.14462 (2024)  [[arxiv]](https://arxiv.org/pdf/2412.14462) [[code]](https://github.com/KaKituken/affordance-aware-any)\n+ Weijing Tao, Xiaofeng Yang, Biwen Lei, Miaomiao Cui, Xuansong Xie, Guosheng Lin: \"*MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion Prior.*\" arXiv:2409.10090 (2024) [[arxiv]](https://arxiv.org/pdf/2409.10090.pdf) [[code]](https://github.com/weijing-tao/MotionCom)\n+ Daniel Winter, Matan Cohen, Shlomi Fruchter, Yael Pritch, Alex Rav-Acha, Yedid Hoshen: \"*ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion.*\"  ECCV (2024) [[arxiv]](https://arxiv.org/pdf/2403.18818) [[paper]](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09857.pdf)\n+ Gemma Canet Tarrés, Zhe Lin, Zhifei Zhang, Jianming Zhang, Yizhi Song, Dan Ruta, Andrew Gilbert, John Collomosse, Soo Ye Kim：\"*Thinking Outside the BBox: Unconstrained Generative Object Compositing.*\" ECCV (2024) [[arxiv]](https://arxiv.org/pdf/2409.04559) [[paper]](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07965.pdf)\n+ Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, He Zhang, Wei Xiong, Daniel Aliaga: \"*IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation.*\" CVPR (2024) [[arxiv]](https://arxiv.org/pdf/2403.10701) [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Song_IMPRINT_Generative_Object_Compositing_by_Learning_Identity-Preserving_Representation_CVPR_2024_paper.pdf)\n+ Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, Hengshuang Zhao: \"*AnyDoor: Zero-shot Object-level Image Customization.*\" CVPR (2024) [[arxiv]](https://arxiv.org/pdf/2307.09481) [[paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_AnyDoor_Zero-shot_Object-level_Image_Customization_CVPR_2024_paper.pdf) [[code]](https://github.com/damo-vilab/AnyDoor) \n+ Vishnu Sarukkai, Linden Li, Arden Ma, Christopher Re, Kayvon Fatahalian: \"*Collage Diffusion.*\" WACV (2024) [[arxiv]](https://arxiv.org/pdf/2303.00262) [[paper]](https://openaccess.thecvf.com/content/WACV2024/papers/Sarukkai_Collage_Diffusion_WACV_2024_paper.pdf) [[code]](https://github.com/VSAnimator/collage-diffusion) \n+ Ziyang Yuan, Mingdeng Cao, Xintao Wang, Zhongang Qi, Chun Yuan, Ying Shan: \"*CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models.*\" ACM MM (2024) [[arxiv]](https://arxiv.org/pdf/2310.19784) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3664647.3681396) [[code]](https://github.com/TencentARC/CustomNet) \n+ Bo Zhang, Yuxuan Duan, Jun Lan, Yan Hong, Huijia Zhu, Weiqiang Wang, Li Niu: \"*ControlCom: Controllable Image Composition using Diffusion Model.*\" arXiv:2308.10040 (2023) [[arxiv]](https://arxiv.org/pdf/2308.10040.pdf) [[code]](https://github.com/bcmi/ControlCom-Image-Composition)\n+ Xin Zhang, Jiaxian Guo, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa: \"*Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model.*\" arXiv:2306.07596 (2023) [[arxiv]](https://arxiv.org/pdf/2306.07596.pdf) \n+ Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen: \"*Paint by Example: Exemplar-based Image Editing with Diffusion Models.*\" CVPR (2023) [[arxiv]](https://arxiv.org/pdf/2211.13227.pdf) [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Paint_by_Example_Exemplar-Based_Image_Editing_With_Diffusion_Models_CVPR_2023_paper.pdf) [[code]](https://arxiv.org/pdf/2211.13227.pdf) \n+ Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen, Brian Price, Jianming Zhang, Soo Ye Kim, Daniel Aliaga: \"*ObjectStitch: Generative Object Compositing.*\" CVPR (2023) [[arxiv]](https://arxiv.org/pdf/2212.00932.pdf) [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Song_ObjectStitch_Object_Compositing_With_Diffusion_Model_CVPR_2023_paper.pdf) [[code]](https://github.com/bcmi/ObjectStitch-Image-Composition)\n+ Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A. Efros, Krishna Kumar Singh: \"*Putting People in Their Place: Affordance-Aware Human Insertion into Scenes.*\" CVPR (2023) [[arxiv]](https://arxiv.org/pdf/2304.14406) [[paper]](https://sumith1896.github.io/affordance-insertion/static/paper/affordance_insertion_cvpr2023.pdf) [[code]](https://github.com/adobe-research/affordance-insertion)\n\n####  Require object-centric finetuning\n+ Jiaxuan Chen, Bo Zhang, Qingdong He, Jinlong Peng, Li Niu: \"*CareCom: Generative Image Composition with Calibrated Reference Features*\", AAAI (2026) [[arxiv]](https://www.arxiv.org/pdf/2511.11060) [[paper]](https://ojs.aaai.org/index.php/AAAI/article/download/37278/41240) [[code]](https://github.com/bcmi/Carecom-Image-Composition)\n+ Nataniel Ruiz, Yuanzhen Li, Neal Wadhwa, Yael Pritch, Michael Rubinstein, David E. Jacobs, Shlomi Fruchter: \"*Magic Insert: Style-Aware Drag-and-Drop.*\" ICCV (2025) [[arxiv]](https://arxiv.org/pdf/2407.02489) [[paper]](https://openaccess.thecvf.com/content/ICCV2025/papers/Ruiz_Magic_Insert_Style-Aware_Drag-and-Drop_ICCV_2025_paper.pdf)\n+ Zhekai Chen, Wen Wang, Zhen Yang, Zeqing Yuan, Hao Chen, Chunhua Shen: \"*FreeCompose: Generic Zero-Shot Image Composition with Diffusion Prior.*\" ECCV (2024) [[arxiv]](https://arxiv.org/pdf/2407.04947) [[paper]](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/02529.pdf) [[code]](https://github.com/aim-uofa/FreeCompose)\n+ Lingxiao Lu, Bo Zhang, Li Niu: \"*DreamCom: Finetuning Text-guided Inpainting Model for Image Composition.*\" arXiv:2309.15508 (2023) [[arxiv]](https://arxiv.org/pdf/2309.15508.pdf) [[code]](https://github.com/bcmi/DreamCom-Image-Composition)\n+ Tianle Li, Max Ku, Cong Wei, Wenhu Chen: \"*DreamEdit: Subject-driven Image Editing.*\" TMLR (2023) [[arxiv]](https://arxiv.org/pdf/2306.12624.pdf) [[paper]](https://openreview.net/pdf?id=P9haooN9v2) [[code]](https://github.com/DreamEditBenchTeam/DreamEdit)\n\n\n\n## Other Resources\n\n+ [Awesome-Image-Composition](https://github.com/bcmi/Awesome-Object-Insertion)\n\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/bcmi%2Fawesome-generative-image-composition/projects"}