GI-AEE: GAN Inversion Based Attentive Expression Embedding Network For Facial Expression Editing

Yun Zhang, R. Liu, Yifan Pan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai
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引用次数: 3

Abstract

Facial expression editing aims to modify facial expression by specific conditions. Existing methods adopt an encoder-decoder architecture under the guidance of expression condition to process the desired expression. However, these methods always tend to produce artifacts and blurs in expression-intensive regions due to simultaneously modifying images in expression changed regions and ensuring the consistency of other attributes with the source image. To address these issues, we propose a GAN inversion based Attentive Expression Embedding Network (GI-AEE) for facial expression editing, which decouples this task utilizing GAN inversion to alleviate the strong effect of the source image on the target image and produces high-quality expression editing results. Furthermore, different from existing methods that directly embed the expression condition into the network, we propose an Attentive Expression Embedding module to embed corresponding expression vectors into different facial regions, producing more plausible results. Qualitative and quantitative experiments demonstrate our method outperforms the state-of-the-art expression editing methods.
基于GAN反演的面部表情关注嵌入网络
面部表情编辑的目的是根据特定的条件对面部表情进行修改。现有方法采用在表达式条件指导下的编码器-解码器结构来处理期望的表达式。然而,由于这些方法在修改表达变化区域的图像的同时,还要保证其他属性与源图像的一致性,因此往往会在表达密集区域产生伪影和模糊。为了解决这些问题,我们提出了一种基于GAN反演的专注表达嵌入网络(GI-AEE)用于面部表情编辑,该网络利用GAN反演来解耦该任务,以减轻源图像对目标图像的强烈影响,并产生高质量的表情编辑结果。此外,与现有的直接将表情条件嵌入网络的方法不同,我们提出了一个专注的表情嵌入模块,将相应的表情向量嵌入到不同的面部区域,从而产生更可信的结果。定性和定量实验表明,我们的方法优于最先进的表达编辑方法。
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