Katherine Xu

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Our method can recover the hidden pixels of objects in diverse images. Occluders may be co-occurring (a person on a surfboard), accidental (a cat in front of a microwave), the image boundary (giraffe), or a combination of these scenarios. The pink outline indicates an occluder object.

We use pretrained diffusion inpainting models, and no additional training is required!

• Stay tuned for our code release!

• Requirements
• Setup
• Citation

• Python 3.10
• Docker

1. Clone this amodal repository, and run cd Grounded-Segment-Anything.

2. In the Dockerfile, change all instances of /home/appuser to your path for the amodal repository.

3. Run make build-image.

4. Start and attach to a docker container from the image gsa:v0. Then, navigate to the amodal repository.

5. Run ./install.sh to finish setup and download model checkpoints.

1. Run ./download_dataset.sh to download the COCO dataset.

1. In ./main.sh, modify input_dir to your folder path for the images.

2. Run ./main.sh. You may need to use chmod to change the file permissions first.

If you find our work useful, please cite our paper:

@inproceedings{xu2024amodal,
  title={Amodal completion via progressive mixed context diffusion},
  author={Xu, Katherine and Zhang, Lingzhi and Shi, Jianbo},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9099--9109},
  year={2024}
}