Shadow Generation for Composite Image in Real-world Scenes

TL;DR

This paper introduces a new dataset and a neural network for generating realistic shadows in composite images, improving visual plausibility in image editing.

Contribution

The work presents a novel shadow generation network and a real-world shadow dataset for enhanced composite image realism.

Findings

Effective shadow generation demonstrated on DESOBA dataset

Improved realism in composite images with generated shadows

Code and dataset publicly available for further research

Abstract

Image composition targets at inserting a foreground object into a background image. Most previous image composition methods focus on adjusting the foreground to make it compatible with background while ignoring the shadow effect of foreground on the background. In this work, we focus on generating plausible shadow for the foreground object in the composite image. First, we contribute a real-world shadow generation dataset DESOBA by generating synthetic composite images based on paired real images and deshadowed images. Then, we propose a novel shadow generation network SGRNet, which consists of a shadow mask prediction stage and a shadow filling stage. In the shadow mask prediction stage, foreground and background information are thoroughly interacted to generate foreground shadow mask. In the shadow filling stage, shadow parameters are predicted to fill the shadow area. Extensive experiments on our DESOBA dataset and real composite images demonstrate the effectiveness of our proposed method. Our dataset and code are available at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA.