DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation
Qingyang Liu Jianting Wang Li Niu
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.