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<img src="figs/maggie.png" alt="maggie" width="28"/> MaGGIe: Mask Guided Gradual Human Instance Matting

[Project Page] [Hugging Face] [Paper] [Model Zoo] [Datasets]

Instance-awareness alpha human matting with binary mask guidance for images and video

Accepted at CVPR 2024

Chuong Huynh, Seoung Wug Oh, Abhinav Shrivastava, Joon-Young Lee

Work is a part of Summer Internship 2023 at Adobe Research

Release

• [2024/04/10] Demo on Huggingface is ready!
• [2024/04/07] Code, dataset and paper are released!
• [2024/04/04] Webpage is up!

Contents

• Install
• MaGGIe Weights
• Demo
• Evaluation
• Training
• Citation

Install

We tested our model on Linux CUDA 12.0, for other OS, the framework should work fine!

1. Clone this repository and navigate to MaGGIe folder:

git clone https://github.com/hmchuong/MaGGIe.git
cd MaGGIe

2. Make sure you install CUDA 12.0 and install dependencies via:

conda create -n maggie python=3.8 pip
conda activate maggie
conda install -y pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt

MaGGIe Weights

Please check our Model Zoo for all public MaGGIe checkpoints, and instructions for how to use weights.

Demo

Please check Demo for more information.

Evaluation

Please check the Model Zoo for all model weight information.

M-HIM2K and HIM2K

The script scripts/test_maggie_image.sh contains the full evaluation on the whole M-HIM2K. The results.csv in the log directory contains all the results needed. To get the number in the paper, you can run this command on 4 GPUs:

sh scripts/eval_image.sh configs/maggie_image.yaml 4  maggie

You can also run one subset (e.g, natural) with one model mask (e.g, r50_c4_3x) by:

NGPUS=4
CONFIG=configs/maggie_image.yaml
SUBSET=natural
MODEL=r50_c4_3x
torchrun --standalone --nproc_per_node=$NGPUS tools/main.py --config $CONFIG --eval-only \
                                                name eval_full \
                                                dataset.test.split $SUBSET \
                                                dataset.test.downscale_mask False \
                                                dataset.test.mask_dir_name masks_matched_${MODEL} \
                                                test.save_results False \
                                                test.postprocessing False \
                                                test.log_iter 10

If you want to save the alpha mattes, please set test.save_results True and change the test.save_dir

V-HIM60

The script scripts/test_maggie_video.sh contains the full evaluation on the V-HIM60. This evaluation is only compatible with a single GPU. To get the number in the paper, you can run this command:

sh scripts/eval_video.sh configs/maggie_video.yaml maggie

If you want to evaluate on a subset (e.g, easy), you can run:

CONFIG=configs/maggie_video.yaml
SUBSET=easy
torchrun --standalone --nproc_per_node=1 tools/main.py --config $CONFIG --eval-only \
                    name eval_full \
                    dataset.test.split comp_$SUBSET \
                    test.save_results False \
                    test.log_iter 10

If you want to save the alpha mattes, please set test.save_results True and change the test.save_dir.

Training

1. Please firstly follow DATASET to prepare the training data.

2. Download pretrained weights of the encoder from GCA-Matting

3. Training the image instance matting.

It is recommended to use 4 A100-40GB GPUs or (any GPUs with VRAM>=24GB) for this step. Please check the config and set wandb settings to your project.

NAME=<name of the experiment>
NGPUS=4
torchrun --standalone --nproc_per_node=$NGPUS tools/main.py \
                    --config configs/maggie_image.yaml \
                    --precision 16 name $NAME model.weights ''

If you want to resume training from the last checkpoint, you can turn on train.resume_last or set train.resume to the checkpoint folder you want to resume from. You can also set wandb.id to continue logging to the same experiment id.

4. Training the video instance matting

It is recommend to use 8 A100-80GB GPUs for this step. Please check the config and set `wandb to your project.

NAME=<name of the experiment>
PRETRAINED=<best weight from previous step>
NGPUS=8
torchrun --standalone --nproc_per_node=$NGPUS tools/main.py \
                    --config configs/maggie_video.yaml \
                    --precision 16 name $NAME model.weights $PRETRAINED

If you want to resume training from the last checkpoint, you can turn on train.resume_last or set train.resume to the checkpoint folder you want to resume from. You can also set wandb.id to continue logging to the same experiment id.

Citation

If you find MaGGIE useful for your research and applications, please cite using this BibTeX:

@inproceedings{huynh2024maggie,
  title={MaGGIe: Masked Guided Gradual Human Instance Matting},
  author={Huynh, Chuong and Oh, Seoung Wug and and Shrivastava, Abhinav and Lee, Joon-Young},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

Baselines

We also provide baselines' training and evaluation scripts at BASELINES

Terms of Use

The project is under the CC BY-NC 4.0 License for non-commercial purpose only.

Acknowledgement

We thank Markus Woodson for his early project discussion. Our code is based on the OTVM and MGM.

Contact

If you have any question, please drop an email to chuonghm@umd.edu or create an issue on this repository.