A curated list of resources including papers, datasets, and relevant links pertaining to object shadow generation, which aims to generate plausible shadow for the inserted foreground object in a composite image.
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.
- Yan Hong, Li Niu, Jianfu Zhang: "Shadow Generation for Composite Image in Real-world Scenes." AAAI (2022) [arXiv] [dataset]
- 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]
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Bin Liao, Yao Zhu, Chao Liang, Fei Luo, Chunxia Xiao: "Illumination animating and editing in a single picture using scene structure estimation." Computers & Graphics (2019) [pdf]
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Bin Liu, Kun Xu, Ralph R. Martin: "Static scene illumination estimation from videos with applications." Journal of Computer Science and Technology (2017) [pdf]
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Kevin Karsch, Kalyan Sunkavalli, Sunil Hadap, Nathan Carr, Hailin Jin, Rafael Fonte, Michael Sittig, David Forsyth: "Automatic scene inference for 3d object compositing." ACM Transactions on Graphics (2014) [arXiv]
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Kevin Karsch, Varsha Hedau, David Forsyth, Derek Hoiem: "Rendering synthetic objects into legacy photographs." ACM Transactions on Graphics (2011) [arXiv]
- Shadow-AR: 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: It contains 840 training images with totally 2,999 object-shadow pairs and 160 test images with totally 624 object-shadow pairs. [pdf] [link]