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<h2>SimSwap: An Efficient Framework For High Fidelity Face Swapping</h2>
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<h4>Renwang Chen<sup>*</sup>, Xuanhong Chen<sup>*</sup>, Bingbing Ni, Yanhao Ge</h4>
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<h4>Shanghai Jiao Tong University, Tencent, China</h4>
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<h5><sup>*</sup>Equal contribution.</h5>
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<p>
<a class="btn btn-primary btn-lg" href="https://github.com/neuralchen/SimSwap" role="button" target="_blank"> <span class="glyphicon glyphicon-star" aria-hidden="true"></span> Code &raquo;</a>
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<p><a class="btn btn-primary btn-lg" href="#downloads" role="button"><span class="glyphicon glyphicon-cloud-download" aria-hidden="true"></span> Download &raquo;</a></p>
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<p><a class="btn btn-primary btn-lg" href="https://arxiv.org/abs/2106.06340" role="button" target="_blank"><span class="glyphicon glyphicon-save-file" aria-hidden="true"></span> Paper &raquo;</a></p>
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<h2>Abstract</h2>
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<p>
We propose an efficient framework, called <strong><i>Simple Swap (SimSwap)</i></strong>, aiming for generalized and high fidelity face swapping. In contrast to previous approaches that either lack the ability to generalize to arbitrary
identity or fail to preserve attributes like facial expression and gaze direction, our framework is capable of transferring the identity of an arbitrary source face into an arbitrary target face while preserving the attributes of the
target face. We overcome the above defects in the following two ways. First, we present the <i>ID Injection Module (IIM)</i> which transfers the identity information of the source face into the target face at feature level. By using
this module, we extend the architecture of an identityspecific face swapping algorithm to a framework for arbitrary face swapping. Second, we propose the <i>Weak Feature Matching Loss</i> which efficiently helps our framework to preserve
the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our <i>SimSwap</i> is able to achieve competitive identity performance while preserving attributes better than previous state-of-the-art
methods. The code is already available on github: https://github.com/neuralchen/SimSwap.
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<h2>SimSwap Demo</h2>
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<h3>Single Face Video Swap</h3>
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<h3>Multiple Faces Video Swap</h2>
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[TODO]
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<h3>An overview of model capacity</h2>
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<h2>Downloads</h2>
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<a class="btn btn-success btn-lg" href="https://github.com/neuralchen/SimSwap" role="button" target="_blank"> <span class="glyphicon glyphicon-hdd" aria-hidden="true"></span> Donwnload via Google Drive &raquo;</a>
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<span class="label label-info">password: xxxx</span>
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<h2>Citation</h2>
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<!-- @misc{chen2020rainnet,<br/> &ensp;&ensp;title={RainNet: A Large-Scale Dataset for Spatial Precipitation Downscaling},<br/> &ensp;&ensp;author={Xuanhong Chen and Kairui Feng and Naiyuan Liu and Yifan Lu and Zhengyan Tong and Bingbing
Ni and Ziang Liu and Ning Lin},<br/> &ensp;&ensp;year={2020},
<br/> &ensp;&ensp;eprint={2012.09700},
<br/> &ensp;&ensp;archivePrefix={arXiv},
<br/> &ensp;&ensp;primaryClass={cs.CV}
<br/> } -->
@article{Chen_2020,
<br/> &ensp;&ensp;title={SimSwap},
<br/> &ensp;&ensp;ISBN={9781450379885},
<br/> &ensp;&ensp;url={http://dx.doi.org/10.1145/3394171.3413630},
<br/> &ensp;&ensp;DOI={10.1145/3394171.3413630},
<br/> &ensp;&ensp;journal={Proceedings of the 28th ACM International Conference on Multimedia},
<br/> &ensp;&ensp;publisher={ACM},
<br/> &ensp;&ensp;author={Chen, Renwang and Chen, Xuanhong and Ni, Bingbing and Ge, Yanhao},
<br/> &ensp;&ensp;year={2020},
<br/> &ensp;&ensp;month={Oct}
<br/> }
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<h2>Contact</h2>
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<p>Please concat Renwang Chen <span class="label label-primary">applebananac@sjtu.edu.cn</span> and Xuanhong Chen <span class="label label-primary">xuanhongchenzju@outlook.com</span> for questions about the paper. </p>
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<h3>High Resolution Precipitation Maps</h2>
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<h3>Low Resolution Precipitation Maps</h2>
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