TL;DR
Unlike classical CNN approaches, the proposed model employs adversarial training to enhance realism and supports smooth stylization control ranging from content preservation to full stylization, and provides user-controllable interpolation and better inference time.
StyleSmith is proposed, a diffusion framework for high-fidelity signature style transfer that supports local style transfer, texture transfer, and style-guided text-to-image generation, demonstrating strong academic innovation and industrial value.
Image style transfer focuses on rendering a content image with the visual appearance of a reference artwork while keeping its main semantic layout unchanged. StyleID enables image-guided stylization without additional training by recording attention features during DDIM inversion, yet its results may still suffer from...
Neural style transfer (NST) commonly relies on pretrained convolutional neural networks (CNNs) to extract content and style representations, yet the effect of CNN backbone selection on the perceived quality of stylized images remains underexplored. This paper presents an empirical study of five CNN backbones for NST ac...
A novel self-supervised framework that enables granular control over image generation through a visual abstraction set that provides a richer, more flexible paradigm for creative design compared to state-of-the-art baselines across diverse styles and compositions is introduced.
Video stylization typically transforms entire scenes, while local artistic editing requires precise region--style association, spatial control, and temporal consistency. We introduce LociArt, a unified paradigm for free-style local video stylization that explicitly specifies where to stylize and what style to apply whi...
Artistic style classification remains challenging because visually similar styles often exhibit subtle differences in brushstrokes, textures, colors, and global composition. To address this problem, this paper proposes a lightweight artistic style classification framework based on a MobileNet–Transformer Hybrid (MTH)....
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