Skip to yearly menu bar Skip to main content


Shadow Generation for Composite Image Using Diffusion Model

Qingyang Liu · Junqi You · Jian-Ting Wang · Xinhao Tao · Bo Zhang · Li Niu

Arch 4A-E Poster #322
[ ]
Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However, they are struggling to generate shadows with accurate shapes and intensities, hindered by data scarcity and the inherent task complexity. In this paper, we resort to foundational model with rich prior knowledge of natural shadow images. Specifically, we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover, we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model in shadow generation task.

Live content is unavailable. Log in and register to view live content