关键词: 生成对抗网络, 语义向量, 人脸属性编辑, 属性边界向量, 动态卷积

Abstract: Unsupervised facial attribute editing methods based on the latent space of generative adversarial networks (GANs) offer advantages of high efficiency and annotation-free operation, yet they still face challenges in terms of attribute disentanglement and controllability—for instance, modifying a specific facial attribute may inadvertently alter other attributes, compromising editing quality, while precise control over the degree of attribute modification remains difficult. To address these issues, an Autoencoder-based Unsupervised Face Attribute Editing (AUFAE) method was proposed, which achieved precise facial attribute editing by learning effective semantic vectors in the latent space. Specifically, a Dynamic Convolutional Autoencoder Network (DCAE-Net) was employed as the backbone, where Dynamic Convolution (DyConv) was utilized by the encoder to adaptively extract local latent-space features, thereby enabling the learning of semantically meaningful vectors with localized characteristics. A Channel Attention (CA) mechanism was incorporated into the decoder to establish nonlinear dependencies between channels, allowing the model to autonomously focus on feature channels relevant to different semantics and enhancing the independence of learned semantic vectors. To improve disentanglement and controllability, an attribute boundary vector-based loss function was introduced to train the DCAE-Net. Additionally, a soft orthogonality loss was applied to ensure mutual independence among semantic vectors, further boosting disentanglement performance. Experiments conducted on three pre-trained GAN models compare AUFAE with three state-of-the-art face attribute editing methods. The experimental results demonstrate that compared to the supervised method InterFaceGAN, the proposed AUFAE achieves an average reduction of 9% in the Learned Perceptual Image Patch Similarity （LPIPS）metric and an average improvement of 7% in the Structural Similarity Index Measure （SSIM） metric. When compared to the unsupervised method SDFlow, AUFAE shows an average reduction of 5% in LPIPS and an average improvement of 5% in SSIM. In terms of visual perception, AUFAE also did not exhibit any attribute coupling phenomenon during the facial attribute editing process. The above results demonstrate that AUFAE can effectively mitigate the issue of attribute coupling in facial editing and achieve more precise face attribute manipulation.

Key words: Generative Adversarial Network (GAN), semantic vectors, face attribute Editing, attribute boundaries vector, Dynamic Convolution (DyConv)