Spatially Face Manipulation on Autoencoder Space
首发时间:2021-01-29
Abstract:While the quality of autoencoder image reconstruction and the disentanglement of autoencoder image representation have improved tremendously in recent years, the ability to manipulate the output image by controlling the latent space which represents images is still limited. Manipulation on the specific region of an image is also lack of study.This paper presents two novel face editing strategies that allow changing the semantic information of any arbitrary regions of images by manipulating the spatially disentangled representations of face images. One presents a new normalization, adaptive region normalization (AdaRN) to allow representation collaging, the other shows that the principal components computed by patch Principal Components Analysis (patch PCA) has meaningful information. The principal components allow to edit the specific region of image and control its semantic information. It was based on a well-trained autoencoder network called swapping autoencoder proposed recently.The two strategies can edit face images over an arbitrary region using weak supervision on a well-trained model. Experiments on FFHQ dataset show that any arbitrary regions such as mouth, eyes and eyebrows can be edited naturally using our strategies. Extensive results on the FFHQ dataset suggest that our strategy can not only edit face images flexibly but also require less effort for image labeling and model training tasks.
keywords: AutoEncoder, face manipulation, feature collage, latent space
点击查看论文中文信息
基于自编码器的人脸空间操作
摘要:尽管近年来基于自编码器的图像重建质量和基于自编码器的图像表示解耦性有了很大的提高,但是通过控制图像的潜在空间进行图像的编辑能力仍然是有限的,如何编辑图像特定区域也缺乏一定的研究。本文提出了两种新的人脸编辑策略,通过操作人脸图像的潜在空间,可以改变目标图像任意区域的语义信息。它基于最近提出的一个名为swapping autoencoder的自编码器网络。一种是提出了一种新的归一化方法自适应区域归一化(AdaRN)在特征层面上实现图像的拼接,另一种是使用补丁主成分分析(patch-PCA)获取主成分向量。该主成分可以作为图像编辑方向控制图像特定区域的语义信息。这两种策略可以在训练好的模型上使用弱监督在任意区域编辑人脸图像。在FFHQ数据集上的实验表明,该策略可以对任意区域(如嘴巴、眼睛和眉毛)进行自然编辑。在FFHQ数据集上的大量实验结果表明,我们的策略不仅可以灵活地编辑人脸图像,并且极大的减少了在模型训练和数据获取上耗费的精力 。
引用
No.****
同行评议
勘误表
基于自编码器的人脸空间操作
评论
全部评论0/1000