A curated list of papers, code and resources pertaining to image composition

Overview

Awesome Image Composition Awesome

A curated list of resources including papers, datasets, and relevant links pertaining to image composition.

Contributing

Contributions 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.

Table of Contents

Surveys

  • Li 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 preprint arXiv:2106.14490 (2021). [arXiv]

Papers

Image blending

  • Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang: "GP-GAN: Towards Realistic High-Resolution Image Blending." ACM MM (2019) [arXiv] [code]
  • Lingzhi Zhang, Tarmily Wen, Jianbo Shi: "Deep Image Blending." WACV (2020) [pdf] [arXiv] [code]

Image harmonization

  • Jun Ling, Han Xue, Li Song, Rong Xie, Xiao Gu: "Region-Aware Adaptive Instance Normalization for Image Harmonization." CVPR (2021) [pdf] [supp] [arXiv] [code].
  • Zonghui Guo, Haiyong Zheng, Yufeng Jiang, Zhaorui Gu, Bing Zheng: "Intrinsic Image Harmonization." CVPR (2021) [pdf] [supp] [code].
  • Wenyan Cong, Li Niu, Jianfu Zhang, Jing Liang, Liqing Zhang: "BargainNet: Background-Guided Domain Translation for Image Harmonization." ICME (2021) [arXiv] [code].
  • Konstantin Sofiiuk, Polina Popenova, Anton Konushin: "Foreground-aware Semantic Representations for Image Harmonization." WACV (2021) [pdf] [supp] [arXiv] [code]
  • Guoqing Hao, Satoshi Iizuka, Kazuhiro Fukui: "Image Harmonization with Attention-based Deep Feature Modulation." BMVC (2020) [pdf] [supp] [code]
  • Wenyan Cong, Jianfu Zhang, Li Niu, Liu Liu, Zhixin Ling, Weiyuan Li, Liqing Zhang: "DoveNet: Deep Image Harmonization via Domain Verification." CVPR (2020) [pdf] [supp] [arXiv] [code].
  • Xiaodong Cun, Chi-Man Pun: "Improving the Harmony of the Composite Image by Spatial-Separated Attention Module." IEEE Trans. Image Process. 29: 4759-4771 (2020) [pdf] [arXiv] [code]
  • Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang: "Deep Image Harmonization." CVPR (2017) [pdf] [supp] [arXiv] [code]

Shadow generation

  • Daquan Liu, Chengjiang Long, Hongpan Zhang, Hanning Yu, Xinzhi Dong, Chunxia Xiao: "ARshadowGAN: Shadow generative adversarial network for augmented reality in single light scenes." CVPR (2020) [pdf] [code].

  • Shuyang Zhang, Runze Liang, Miao Wang: "ShadowGAN: Shadow synthesis for virtual objects with conditional adversarial networks." Computational Visual Media (2019) [pdf].

  • Fangneng Zhan, Shijian Lu, Changgong Zhang, Feiying Ma, Xuansong Xie: "Adversarial Image Composition with Auxiliary Illumination." ACCV (2020) [pdf].

Object placement and spatial transformation

  • Lingzhi Zhang, Tarmily Wen, Jie Min, Jiancong Wang, David Han, Jianbo Shi: "Learning Object Placement by Inpainting for Compositional Data Augmentation" ECCV (2020) [pdf]

  • Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell: "Compositional GAN: Learning Image-Conditional Binary Composition" International Journal of Computer Vision (2020) [arXiv] [code]

  • Song-Hai Zhang, Zhengping Zhou, Bin Liu, Xi Dong, Peter Hall: "What and Where: A Context-based Recommendation System for Object Insertion" Computational Visual Media (2020) [arXiv]

  • Shashank Tripathi, Siddhartha Chandra, Amit Agrawal, Ambrish Tyagi, James M. Rehg, Visesh Chari: "Learning to Generate Synthetic Data via Compositing" CVPR (2019) [arXiv]

  • Haoshu Fang, Jianhua Sun, Runzhong Wang, Minghao Gou, Yonglu Li, Cewu Lu: "InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting" ICCV (2019) [arXiv] [code]

  • Chen-Hsuan Lin, Ersin Yumer, Oliver Wang, Eli Shechtman, Simon Lucey: "ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing" CVPR (2018) [arXiv] [code]

  • Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz: "Context-Aware Synthesis and Placement of Object Instances" NeurIPS (2018) [arXiv] [code]

  • Fuwen Tan, Crispin Bernier, Benjamin Cohen, Vicente Ordonez, Connelly Barnes: "Where and Who? Automatic Semantic-Aware Person Composition" WACV (2018) [arXiv][code]

  • Tal Remez, Jonathan Huang, Matthew Brown: "learning to segment via cut-and-paste" ECCV (2018) [arXiv] [code]

Occlusion

  • Samaneh Azadi, Deepak Pathak, Sayna Ebrahimi, Trevor Darrell: "Compositional GAN: Learning Image-Conditional Binary Composition." IJCV (2020) [arXiv] [code]
  • Fangneng Zhan, Jiaxing Huang, Shijian Lu, "Hierarchy Composition GAN for High-fidelity Image Synthesis." Transactions on cybernetics (2021) [arXiv]

Datasets

  • iHarmony4 (image harmonization): It contains four subdatasets: HCOCO, HAdobe5k, HFlickr, Hday2night, with a total of 73,146 pairs of unharmonized images and harmonized images. [pdf] [link]
  • GMSDataset (image harmonization): It contains 183 images with image resolution of 1940*1440. It consists of 16 different objects and for each object, one source image and 11 target images in different background scenes and illumination conditions are captured. [pdf] [link] (access code: ekn2)
  • HVIDIT (image harmonization): A dataset built upon VIDIT (Virtual Image Dataset for Illumination Transfer) dataset for image harmonization. It contains 3007 images of 276 scenes for training and 329 images of 24 scenes for testing. [pdf] [link]
  • RHHarmony (image harmonization): A rendered image harmonization dataset, which contains 15000 ground-truth rendered images and has the potential to generate 135000 composite rendered images. [pdf] [link]
  • Shadow-AR (shadow generation): It contains 3,000 quintuples, Each quintuple consists of 5 images 640×480 resolution: a synthetic image without the virtual object shadow and its corresponding image containing the virtual object shadow, a mask of the virtual object, a labeled real-world shadow matting and its corresponding labeled occluder. [pdf] [link]
  • DESOBA (shadow generation): It contains 840 training images with totally 2,999 object-shadow pairs and 160 test images with totally 624 object-shadow pairs. [pdf] [link]
  • OPA (object placement): It contains 62,074 training images and 11,396 test images, in which the foregrounds/backgrounds in training set and test set have no overlap. The training (resp., test) set contains 21,351 (resp.,3,566) positive samples and 40,724 (resp., 7,830) negative samples. [pdf] [link]

Other resources

Owner
BCMI
Center for Brain-Like Computing and Machine Intelligence, Shanghai Jiao Tong University.
BCMI
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