CLeaR: A Unified Framework for Resolving the Leakage-Degradation Dilemma in Style Transfer
Style transfer aims to render target content in the style of a reference image, but existing methods often suffer from content leakage, where objects, layouts, or semantics from the style reference appear in the generated output. Although prior data-driven and training-free methods can reduce leakage, they often face a leakage-degradation dilemma: stronger content suppression may weaken style fidelity, while richer style preservation may reintroduce unwanted reference content. We identify this dilemma across the full style-transfer pipeline, including feature separation, feature-space grounding, and diffusion generation. To address these issues, we propose CLeaR, a training-free framework...
| Authors | Teng Zhou, Yunhao Chen |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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