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<h1 align="center">Bridging Composite and Real: Towards End-to-end Deep Image Matting [IJCV-2022]</h1> <p align="center"> <a href="https://arxiv.org/pdf/2010.16188.pdf"><img src="demo/src/icon/arXiv-Paper.svg" ></a> <a href="https://link.springer.com/article/10.1007/s11263-021-01541-0"><img src="demo/src/icon/publication-Paper.svg" ></a> <a href="https://colab.research.google.com/drive/1EaQ5h4u9Q_MmDSFTDmFG0ZOeSsFuRTsJ?usp=sharing"><img src="demo/src/icon/colab-badge.svg"></a> <a href="https://opensource.org/licenses/MIT"><img src="demo/src/icon/license-MIT.svg"></a> <a href="https://paperswithcode.com/sota/image-matting-on-am-2k"><img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/end-to-end-animal-image-matting/image-matting-on-am-2k"></a> <a href="https://www.youtube.com/watch?v=FJPm4YQOEyo"><img src="demo/src/icon/youtube-demo.svg"></a> <a href="https://www.bilibili.com/video/BV1X34y1o7nK"><img src="demo/src/icon/bilibili-demo.svg"></a> <!-- <a href="https://paperswithcode.com/sota/image-matting-on-aim-500?p=end-to-end-animal-image-matting"><img src="https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/end-to-end-animal-image-matting/image-matting-on-aim-500"></a> --> </p> <h4 align="center">This is the official repository of the paper <a href="https://arxiv.org/abs/2010.16188">Bridging Composite and Real: Towards End-to-end Deep Image Matting</a>.</h4> <h5 align="center"><em>Jizhizi Li^1&#8727;, Jing Zhang^1&#8727;, Stephen J. Maybank², and Dacheng Tao¹</em></h5> <h6 align="center">1 The University of Sydney, Sydney, Australia; 2 Birkbeck College, University of London, U.K.</h6> <h6 align="center">IJCV 2022 (DOI 10.1007/s11263-021-01541-0)</h6> <p align="center"> <a href="#demo-on-google-colab">Google Colab Demo</a> | <a href="#introduction">Introduction</a> | <a href="#gfm">GFM</a> | <a href="#am-2k">AM-2k</a> | <a href="#bg-20k">BG-20k</a> | <a href="#results-demo">Results Demo</a> | <a href="https://github.com/JizhiziLi/GFM/tree/master/core">Train and Test</a> | <a href="#inference-code---how-to-test-on-your-images">Inference Code</a> | <a href="#statement">Statement</a> </p> <img src="demo/src/homepage/spring.gif" width="25%"><img src="demo/src/homepage/summer.gif" width="25%"><img src="demo/src/homepage/autumn.gif" width="25%"><img src="demo/src/homepage/winter.gif" width="25%"> *** > ><h3><strong><i>馃殌 News</i></strong></h3> > > [2021-11-12]: The training code, test code and all the pretrained models are released in this [code-base page](https://github.com/JizhiziLi/GFM/tree/master/core). > > [2021-10-22]: The paper has been accepted by the International Journal of Computer Vision ([IJCV](https://www.springer.com/journal/11263))! 馃帀 > > [2021-09-21]: The datasets <a href="#am-2k"><strong>AM-2k</strong></a> and <a href="#bg-20k"><strong>BG-20k</strong></a> can now be <strong>openly accessed</strong> from the links below (both at Google Drive and at Baidu Wangpan) ! Please follow the dataset release agreements to access. Due to some privacy issues, the dataset <Strong>PM-10k</strong> will be published after privacy-preserving from the project [Privacy-Preserving Portrait Matting (ACM MM 21)](https://dl.acm.org/doi/pdf/10.1145/3474085.3475512). You can refer to this [repo](https://github.com/JizhiziLi/P3M) for access and updates. > > | Dataset | <p>Dataset Link<br>(Google Drive)</p> | <p>Dataset Link<br>(Baidu Wangpan 鐧惧害缃戠洏)</p> | Dataset Release Agreement| > | :----:| :----: | :----: | :----: | > |<strong>AM-2k</strong>|[Link](https://drive.google.com/drive/folders/1SReB9Zma0TDfDhow7P5kiZNMwY9j9xMA?usp=sharing)|[Link](https://pan.baidu.com/s/1M1uF227-ZrYe3MBafqyTdw) (pw: 29r1)|[Agreement (MIT License)](https://jizhizili.github.io/files/gfm_datasets_agreements/AM-2k_Dataset_Release_Agreement.pdf)| > |<strong>BG-20k</strong>|[Link](https://drive.google.com/drive/folders/1ZBaMJxZtUNHIuGj8D8v3B9Adn8dbHwSS?usp=sharing)|[Link](https://pan.baidu.com/s/1DR4uAO5j9rs-sfhn8J7oUQ) (pw: dffp)|[Agreement (MIT License)](https://jizhizili.github.io/files/gfm_datasets_agreements/BG-20k_Dataset_Release_Agreement.pdf)| > > [2020-11-17]: Create <a href="https://colab.research.google.com/drive/1EaQ5h4u9Q_MmDSFTDmFG0ZOeSsFuRTsJ?usp=sharing"><strong>Google Colab</strong></a> demo to benefit users who want to have a try online. > > [2020-11-03]: Publish the <a href="#inference-code-how-to-test-on-your-images">inference code</a> and a [pretrained model](https://drive.google.com/uc?export=download&id=1Y8dgOprcPWdUgHUPSdue0lkFAUVvW10Q) that can be used to test on your own animal images. > > [2020-10-27]: Publish a video demo ([YouTube](https://www.youtube.com/watch?v=FJPm4YQOEyo) | [bilibili](https://www.bilibili.com/video/BV1X34y1o7nK) | [Google drive](https://drive.google.com/file/d/1S5sirgvscz0BwVmlO8GiZudP30DEACbE/view?usp=sharing)) contains motivation, network, datasets, and test results on an animal video. ### Demo on Google Colab <p align="justify"> For those who do not have GPUs in their environment or only want to have a simple try online, you can try our <a href="https://colab.research.google.com/drive/1EaQ5h4u9Q_MmDSFTDmFG0ZOeSsFuRTsJ?usp=sharing">Google Colab</a> demo to generate the results for your images easily.</p> <a href="https://colab.research.google.com/drive/1EaQ5h4u9Q_MmDSFTDmFG0ZOeSsFuRTsJ?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> ## Introduction <p align="justify">This repository contains the code, datasets, models, test results and a video demo for the paper <a href="https://arxiv.org/pdf/2010.16188.pdf">Bridging Composite and Real: Towards End-to-end Deep Image Matting</a>. We propose a novel Glance and Focus Matting network (<strong>GFM</strong>), which employs a shared encoder and two separate decoders to learn both tasks in a collaborative manner for end-to-end image matting. We also establish a novel Animal Matting dataset (<strong>AM-2k</strong>) to serve for end-to-end matting task. Furthermore, we investigate the domain gap issue between composition images and natural images systematically, propose a carefully designed composite route <strong>RSSN</strong> and a large-scale high-resolution background dataset (<strong>BG-20k</strong>) to serve as better candidates for composition.</p> <p align="justify">We have released the train code, the test code, the datasets, and the pretrained models in this <a href="https://github.com/JizhiziLi/GFM/tree/master/core">code-base page</a>. We have also prepared a <a href="https://colab.research.google.com/drive/1EaQ5h4u9Q_MmDSFTDmFG0ZOeSsFuRTsJ?usp=sharing">Google Colab</a> demo and <a href="#inference-code-how-to-test-on-your-images"><i>inference code</i></a> for you to test on our pre-trained models on your own sample images. For the datasets <strong>AM-2k</strong> and <strong>BG-20k</strong>, please follow the sections <a href="#am-2k"><i>AM-2k</i></a> and <a href="#bg-20k"><i>BG-20k</i></a> to access. Besides, we prepare a video demo (<a href="https://www.youtube.com/watch?v=FJPm4YQOEyo">YouTube</a> | <a href="https://www.bilibili.com/video/BV1X34y1o7nK">bilibili</a>) to illustrate the motivation, the network, the datasets, and the test results on an animal video</p> ## GFM The architecture of our proposed end-to-end method <strong>GFM</strong> is illustrated below. We adopt three kinds of <em>Representation of Semantic and Transition Area</em> (<strong>RoSTa</strong>) `-TT, -FT, -BT` within our method.  We trained [GFM](core/gfm.py) with four backbones, `-(r)` (ResNet-34), `-(d)` (DenseNet-121), `-(r2b)` (ResNet-34 with 2 extra blocks), and `-(r')` (ResNet-101). The trained model for each backbone can be downloaded via the link listed below. | Model| GFM(d)-TT | GFM(r)-TT | GFM(r)-FT | GFM(r)-BT |GFM(r2b)-TT | GFM(r')-TT | GFM(d)-RIM | | :----:| :----: | :----: |