Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution

• URL: http://arxiv.org/abs/2204.08339v1
• Date: Wed, 13 Apr 2022 05:35:11 GMT
• Title: Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution
• Authors: Haiming Yu and Hao Zhu and Xiangju Lu and Junhui Liu
• Abstract summary: MobileFSGAN is a novel lightweight GAN for face swap that can run on mobile devices with much fewer parameters while achieving competitive performance. A lightweight encoder-decoder structure is designed especially for image synthesis tasks, which is only 10.2MB and can run on mobile devices at a real-time speed.
• Score: 7.572886749166295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing face swap methods rely heavily on large-scale networks for adequate capacity to generate visually plausible results, which inhibits its applications on resource-constraint platforms. In this work, we propose MobileFSGAN, a novel lightweight GAN for face swap that can run on mobile devices with much fewer parameters while achieving competitive performance. A lightweight encoder-decoder structure is designed especially for image synthesis tasks, which is only 10.2MB and can run on mobile devices at a real-time speed. To tackle the unstability of training such a small network, we construct the FSTriplets dataset utilizing facial attribute editing techniques. FSTriplets provides source-target-result training triplets, yielding pixel-level labels thus for the first time making the training process supervised. We also designed multi-scale gradient losses for efficient back-propagation, resulting in faster and better convergence. Experimental results show that our model reaches comparable performance towards state-of-the-art methods, while significantly reducing the number of network parameters. Codes and the dataset have been released.

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