This is the official repository for the following paper:

Shadow Generation for Composite Image Using Diffusion Model [arXiv]

Qingyang Liu, Junqi You, Jianting Wang, Xinhao Tao, Bo Zhang, Li Niu
Accepted by CVPR 2024.

Our improved version GPSDiffusion can be found here.

DESOBAv2 is a large-scale real-world shadow generation dataset containing object-shadow pairs like DESOBA dataset with 28,573 foreground objects, which is useful for supervised shadow generation methods. It has 21,575 real images with 28,573 object-shadow pairs from outdoor scenes. In the following example images, from left to right, we show the composite image, the foreground object mask, the foreground shadow mask, the background object mask, the background shadow mask, the ground-truth target image.

You can download the full DESOBAv2 Dataset from [Baidu_Cloud] (access code: bcmi) or [Dropbox]. We release two versions: the full-resolution version and 256x256 version.

We also release 22469 unused shadow images [Baidu_Cloud] (access code: bcmi) or [Dropbox], in case that you want to extend DESOBAv2 dataset.

Here we provide PyTorch implementation and the trained model of our SGDiffusion.

• Clone this repo: git clone https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.git
• Download the DESOBAv2 dataset from [Baidu Cloud] (access code: bcmi) or [Dropbox]. Unzip desobav2-256x256.rar to ./data/, and rename it to desobav2.
• Download the checkpoints from [Baidu Cloud] (access code: bcmi) or [Dropbox]. Unzip ckpt.rar to ./data/. Note that we also provide an alternative model DESOBAv2_2.pth, which can replace DESOBAv2.pth in ckpt.rar.

conda env create -f environment.yaml
conda activate SGDiffusion

python train_SGDiffusion.py

python infer_SGDiffusion.py

python post_processing.py

python eval_result.py

• Awesome-Object-Shadow-Generation
• Awesome-Image-Composition