## SimSwap: An Efficient Framework For High Fidelity Face Swapping

#### Renwang Chen*, Xuanhong Chen*, Bingbing Ni, Yanhao Ge

#### Shanghai Jiao Tong University, Tencent, China

##### *Equal contribution.

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## Abstract

We propose an efficient framework, called Simple Swap (SimSwap), 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 ID Injection Module (IIM) 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 Weak Feature Matching Loss which efficiently helps our framework to preserve the facial attributes in an implicit way. Extensive experiments on wild faces demonstrate that our SimSwap 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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## SimSwap Demo

### Single Face Video Swap

### Multiple Faces Video Swap

[TODO]

### An overview of model capacity

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## Citation

@article{Chen_2020,
title={SimSwap},
ISBN={9781450379885},
url={http://dx.doi.org/10.1145/3394171.3413630},
DOI={10.1145/3394171.3413630},
journal={Proceedings of the 28th ACM International Conference on Multimedia},
publisher={ACM},
author={Chen, Renwang and Chen, Xuanhong and Ni, Bingbing and Ge, Yanhao},
year={2020},
month={Oct}
}

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## Contact

Please concat Renwang Chen applebananac@sjtu.edu.cn and Xuanhong Chen xuanhongchenzju@outlook.com for questions about the paper.