标签归档:Face Editing

Interpreting the Latent Space of GANs for Semantic Face Editing

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sampled from a random distribution to a photo-realistic image. Previous work assumes the latent space learned by GANs follows a distributed representation but observes the vector arithmetic phenomenon. In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs. In this framework, we conduct a detailed study on how different semantics are encoded in the latent space of GANs for face synthesis. We find that the latent code of well-trained generative models actually learns a disentangled representation after linear transformations. We explore the disentanglement between various semantics and manage to decouple some entangled semantics with subspace projection, leading to more precise control of facial attributes. Besides manipulating gender, age, expression, and the presence of eyeglasses, we can even vary the face pose as well as fix the artifacts accidentally generated by GAN models. The proposed method is further applied to achieve real image manipulation when combined with GAN inversion methods or some encoder-involved models. Extensive results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable facial attribute representation.

https://openaccess.thecvf.com/content_CVPR_2020/html/Shen_Interpreting_the_Latent_Space_of_GANs_for_Semantic_Face_Editing_CVPR_2020_paper.html

虽然在高质量图像生成任务中GANs取得了许多进展,但是对于GANs是如何将一个从随机分布中采样生成的隐编码映射到一张逼真图像上依然缺乏了解。之前的工作假设GANs学习到的隐空间遵循以向量形式表达的分布式表示。在本文中,我们提出了一种叫做InterFaceGAN的架构,它可以通过解释隐空间的语义来实现面部编辑的功能。在这个架构中,我们仔细研究了在进行面部编辑任务时不同的语义是如何被编码到隐空间。我们发现经过训练的生成模型的隐编码确实可以在线性变化后解构,而且这样在语义之间的解构通过子空间中的映射实现了解纠缠,这能够实现对于面部结构的精细控制。除了可以控制性别,年龄,表情以及是否戴眼镜等面部特征,我们还可以调整面部姿态以及消除GAN模型的生成痕迹。本文提出的模型还可以通过与逆GAN模型或者一些生成模型结合进一步应用在真实图像的编辑任务上。更多的实验结果证明, 在训练面部合成模型的过程中会自发的获得解纠缠以及可控的面部标签表示。

GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing

Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there still lacks of control over the generation process in order to achieve semantic face editing. In addition, it remains very challenging to maintain other face information untouched while editing the target attributes. In this paper, we propose a novel learning framework, called GuidedStyle, to achieve semantic face editing on StyleGAN by guiding the image generation process with a knowledge network. Furthermore, we allow an attention mechanism in StyleGAN generator to adaptively select a single layer for style manipulation. As a result, our method is able to perform disentangled and controllable edits along various attributes, including smiling, eyeglasses, gender, mustache and hair color. Both qualitative and quantitative results demonstrate the superiority of our method over other competing methods for semantic face editing. Moreover, we show that our model can be also applied to different types of real and artistic face editing, demonstrating strong generalization ability. 

https://arxiv.org/pdf/2012.11856v1.pdf

虽然在非条件GAN图像在高品质图像生成领域已经取得了长足进步,现在的生成过程还存在一定的缺陷,例如语义面部编辑任务。另外,在编辑面部图像的时候如何保留非编辑区域信息依然是一个挑战。在本文中,我们提出了针对语义面部编辑任务提出一种新的网络架构,GuidedStyle. 这种架构基于StyleGAN通过一个知识网络来引导图像生成过程。而且我们利用注意力机制使得StyleGAN的生成器可以自适应地选择某一层进行风格编辑。结果表明,我们提出的方法可以利用多样地标签在面部编辑任务中,包括微笑,眼睛,性别,胡子以及发色。