Facial attribute editing has garnered significant attention, yet prevailing methods struggle with achieving precise attribute manipulation while preserving irrelevant details and controlling attribute styles. This challenge primarily arises from the strong correlations between different attributes and the interplay between attributes and identity. In this paper, we propose Semantic Disentangled GAN (SDGAN), a novel method addressing this challenge. SDGAN introduces two key concepts: a semantic disentanglement generator that assigns facial representations to distinct attribute-specific editing modules, enabling the decoupling of the facial attribute editing process, and a semantic mask alignment strategy that confines attribute editing to appropriate regions, thereby avoiding undesired modifications. Leveraging these concepts, SDGAN demonstrates accurate attribute editing and achieves high-quality attribute style manipulation through both latent-guided and reference-guided manners. We extensively evaluate our method on the CelebA-HQ database, providing both qualitative and quantitative analyses. Our results establish that SDGAN significantly outperforms state-of-the-art techniques, showcasing the effectiveness of our approach. To foster reproducibility and further research, we will provide the code for our method.

Recently, facial attribute editing has drawn increasing attention and has achieved significant progress due to Generative Adversarial Network (GAN). Since paired images before and after editing are not available, existing methods typically perform the editing and reconstruction tasks simultaneously, and transfer facial details learned from the reconstruction to the editing via sharing the latent representation space and weights. In this way, they can not preserve those non-targeted regions well during editing. In addition, they usually introduce skip connections between the encoder and decoder to improve image quality at the cost of attribute editing ability. In this paper, we propose a novel method called InterGAN with high-frequency compensation to alleviate above problems. Specifically, we first propose the cross-task interaction (CTI) to fully explore the relationships between editing and reconstruction tasks. The CTI includes two translations: style translation adjusts the mean and variance of feature maps according to style features, and conditional translation utilizes attribute vector as condition to guide feature map transformation. They provide effective information interaction to preserve the irrelevant regions unchanged. Without using skip connections between the encoder and decoder, furthermore, we propose the high-frequency compensation module (HFCM) to improve image quality. The HFCM tries to collect potentially loss information from input images and each down-sampling layers of the encoder, and then re-inject them into subsequent layers to alleviate the information loss. Ablation analysis show the effectiveness of proposed CTI and HFCM. Extensive qualitative and quantitative experiments on CelebA-HQ demonstrate that the proposed method outperforms state-of-the-art methods both in attribute editing accuracy and image quality.

Recently, the relationship among individual pedestrian images and the relationship among pairwise pedestrian images have become attractive for person re-identification (re-ID) as they effectively improve the ability of feature representation. In this paper, we propose a novel method named Hybrid Relationship Network (HRNet) to learn the two types of relationships in a unified framework that makes use of their own advantages. Specifically, for the relationship among individual pedestrian images, we take the features of pedestrian images as the nodes to construct a locally-connected graph, so as to improve the discriminative ability of nodes. Meanwhile, we propose the consistent node constraint to inject the identity information into the graph learning process and guide the information to propagate accurately. As for the relationship among pairwise pedestrian images, we treat the feature differences of pedestrian images as the nodes to construct a fully-connected graph so as to estimate robust similarity of nodes. Furthermore, we propose the inter-graph propagation to alleviate the information loss for the fully-connected graph. Extensive experiments on Market-1501, DukeMTMCreID, CUHK03 and MSMT17 demonstrate that the proposed HRNet outperforms the state-of-the-art methods.