This is the official PyTorch implementation for the CVPRW 24 paper: "ReMOVE: A Reference-free Metric for Object Erasure".
This code was tested with python=3.8.18, pytorch=2.0.1 and torchvision=0.8.2. Please follow the instructions here to install PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
NOTE: This codebase requires SAM to be installed. To install SAM, please follow the instructions here.
pip install git+https://github.com/facebookresearch/segment-anything.git
Start by downloading the model at models/url.txt (~2.4GB). Currently, main.py uses the ViT-H model, but other models can be used by modifying L49 of main.py.
wget -i models/url.txt -P models/
The main.py script takes the following arguments
--image_path: Path to the image.--mask_path: Path to the corresponding mask.--crop(optional) : Crops the image using a bounding box around the object mask.--draw(optional) : Draws a bounding box around the object (saved tomasked.png) if--cropis used.
A set of sample images with objects removed using various methods are provided in ./examples/.
python main.py -i 'examples/sdinpaint/823000000001.jpg' -m 'examples/masks/823000000001.jpg'
python main.py -i 'examples/sdinpaint/823000000001.jpg' -m 'examples/masks/823000000001.jpg' --crop
python main.py -i 'examples/sdinpaint/122000000009.jpg' -m 'examples/masks/122000000009.jpg'
python main.py -i 'examples/sdinpaint/122000000009.jpg' -m 'examples/masks/122000000009.jpg' --crop
If you use ReMOVE or find this work useful for your research, please use the following BibTeX entry.
@InProceedings{Chandrasekar_2024_CVPR,
author = {Chandrasekar$^*$, Aditya and Chakrabarty$^*$, Goirik and Bardhan, Jai and Hebbalaguppe, Ramya and AP, Prathosh},
title = {ReMOVE: A Reference-free Metric for Object Erasure},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024}
}