Enlightening Photographic Style Transfer with a Self-Supervised Photographic Embedding

Photographic style transfer aims to render a content imagewith the retouching style of a reference photo while preserving scenestructure and texture. Unlike artistic style, photographic style is dom-inated by subtle, continuous tone/color changes and smooth spatial ef-fects, which are difficult to represent with semantics-oriented or text-supervised image embeddings. We propose PETAL, a PhotographicEmbedding for Transfer with an Adaptive LUT. PETAL learns a dedi-cated photographic embedding through self-supervised photographic styleaugmentation and uses it to condition a lightweight adaptive 5D neu-ral LUT. The learned embedding outperforms prior representations inphotographic style retrieval, and the full framework enables reference-based style transfer without test-time optimization, achieving strongerquantitative and perceptual performance than state-of-the-art baselines.Project page: https://petal-pku.pages.dev/.