Image composition refers to inserting a foreground object into a background image to obtain a composite image. In this work, we focus on generating plausible shadow for the inserted foreground object to make the composite image more realistic. To supplement the existing small-scale dataset DESOBA, we create a large-scale dataset called DESOBAv2 by using object-shadow detection and inpainting techniques. Specifically, we collect a large number of outdoor scene images with object-shadow pairs. Then, we use pretrained inpainting model to inpaint the shadow region, resulting in the deshadowed images. Based on real images and deshadowed images, we can construct pairs of synthetic composite images and ground-truth target images. Dataset is available at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.
@article{arxiv.2308.09972,
title = {DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation},
author = {Qingyang Liu and Jianting Wang and Li Niu},
journal= {arXiv preprint arXiv:2308.09972},
year = {2023}
}
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arXiv admin note: text overlap with arXiv:2306.17358