一种基于变分自编码器的人脸融合方法

Xiang Li, Jinghao Wen, Anni Chen, Bo Chen
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引用次数: 2

摘要

人脸融合是指将两幅不同的人脸图像融合成一幅新的人脸图像,该图像保留了原图像的面部特征。我们的网络模型结合了变分自编码器(V Ae)和生成对抗网络(GAN),实现了端到端的融合任务。它不仅保证了融合图像的质量(GAN生成的图像清晰逼真),而且不会丢失面部的特定细节(这是VAE的保证)。最后,实验在CelebA数据集上取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Method for Face Fusion Based on Variational Auto-Encoder
Face fusion refers to fuse two different facial images into a new face image that retains the facial features of the original image. Our network model is combine Variational Auto-Encoder(V Ae)and Generative Adversarial Networks(GAN), which achieved the end-to-end fusion task. Not only does it guarantee the quality of the fusion image(the image generated by GAN is sharp and photorealistic), but also doesn't lose specific details of the face(This is guaranteed by VAE). In the end, the experiment achieved a promising result on the CelebA dataset.
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