Image style transfer aims to synthesize visually compelling images by blending the structural content of one image with the artistic style of another. While arbitrary style transfer methods such as AdaIN and WCT offer flexibility, they often suffer from content distortion and style leakage, particularly in complex or cross-domain scenarios. Recent approaches like ArtFlow address these issues through reversible architectures, effectively reducing distortion and leakage while providing consistent reconstruction. However, ArtFlow’s reliance on fixed normalization parameters limits adaptability across diverse content–style pairs, motivating further improvement. In this paper, we propose ISTMAF (Image Style Transfer based on Meta ArtFlow), a scalable and adaptive reversible framework that incorporates Meta ActNorm—a meta-network that dynamically generates input-specific normalization parameters. To further improve the integration of content and style, we introduce an algebraic–geometric parameter fusion strategy in the reverse process, along with a hierarchical aligned style loss to reduce artifacts and enhance visual coherence. Experiments on MS-COCO, WikiArt, and face datasets demonstrate that ISTMAF achieves superior content preservation and style consistency compared to recent state-of-the-art methods. Quantitative evaluations using SSIM and Gram difference further confirm its effectiveness. ISTMAF provides a flexible, high-fidelity solution for style transfer and shows strong generalization potential, paving the way for future extensions in multi-style fusion, video stylization, and 3D applications.
Kao et al. (Fri,) studied this question.