Face photo-sketch translation method based on global-local fast Fourier convolution and adaptive cross-domain attention
TL;DR
Experimental results on the CUFS and CUFSF datasets demonstrate that the proposed method achieves superior visual quality outperforms or ranks second in terms of the LPIPS, FID, and FSIM metrics, validating its effectiveness in high-quality cross-domain image generation.
Abstract
Face photo-sketch translation is a significant task in cross-domain image generation. Traditional methods often struggle to balance global structure and local details, and they lack the ability of adaptive cross-domain feature fusion. To address these challenges, this paper presents a novel image generation method based on generative adversarial networks (GANs). In the early stage of the encoder, a Global-Local Fast Fourier Convolution module is introduced. The global branch employs Fast Fourier Convolution to capture long-range dependencies, while the local branch utilizes depthwise separable and standard convolutions to extract local textures. This parallel approach enables the simultaneous representation of global and local features. Additionally, a bi-directional gated channel attention module is incorporated to aggregate complementary information from both branches, thereby balancing overall consistency and fine detail realism. Furthermore, an Adaptive Cross-Domain Attention module is designed to dynamically select reference image features based on regional correlation: real details are preserved in highly correlated regions, while style alignment and fusion are performed in weakly correlated regions, effectively reducing interference from irrelevant features. Experimental results on the CUFS and CUFSF datasets demonstrate that the proposed method achieves superior visual quality outperforms or ranks second in terms of the LPIPS, FID, and FSIM metrics, validating its effectiveness in high-quality cross-domain image generation.