StyleShot: A Snapshot on Any Style

2024-07-01

arXiv.org

AI Summary

This paper addresses zero-shot generalized image style transfer—transferring images to diverse target styles (e.g., 3D, flat, abstract, fine-grained) across domains without test-time optimization. We propose the first end-to-end generalizable framework for this task. Methodologically, we introduce: (1) a style-decoupled training strategy that enforces orthogonal content and style representations; (2) StyleGallery, a large-scale, structured style dataset enabling semantic style alignment and cross-style generalization; and (3) a content-fused encoder that enhances image-driven style adaptation. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art methods on multi-style transfer benchmarks, achieving superior performance without any test-time fine-tuning. The framework exhibits strong generalization to unseen styles and domains, validating its effectiveness in practical zero-shot settings.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a style-aware encoder and a well-organized style dataset called StyleGallery. With dedicated design for style learning, this style-aware encoder is trained to extract expressive style representation with decoupling training strategy, and StyleGallery enables the generalization ability. We further employ a content-fusion encoder to enhance image-driven style transfer. We highlight that, our approach, named StyleShot, is simple yet effective in mimicking various desired styles, i.e., 3D, flat, abstract or even fine-grained styles, without test-time tuning. Rigorous experiments validate that, StyleShot achieves superior performance across a wide range of styles compared to existing state-of-the-art methods. The project page is available at: https://styleshot.github.io/.

Problem

• Generalized style transfer without tuning
• Style-aware encoder and dataset
• Enhancing image-driven style transfer

Innovation

• Style-aware encoder for representation
• StyleGallery dataset for generalization
• Content-fusion encoder enhancement