通过具有统计特征的StatLUT进行多模态3D LUT生成以实现逼真的风格迁移
Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer
AI总结：
研究针对逼真风格迁移中现有方法的瓶颈，提出StatLUT框架，通过提取统计特征、基于Transformer的Seq2Seq任务预测3D LUT及用H-Diffuser从自然语言提示合成特征，实现多模态逼真风格迁移，性能优于现有方法。
摘要：
逼真的风格迁移（PST）旨在将参考图像的颜色和色调风格转移到内容图像，同时严格保持其结构完整性。现有深度学习方法存在预训练图像编码器导致的语义纠缠，引发不自然的空间扭曲，且当前像素级映射范式常忽略色域拓扑，导致色带，还缺乏多模态能力。为此提出StatLUT框架，绕过传统编码器，用Lab-Extractor提取统计特征，将LUT生成作为基于Transformer的Seq2Seq翻译任务，用多维残差映射器预测3D LUT，还提出H-Diffuser直接从自然语言提示合成统计特征。实验表明StatLUT在视觉质量和量化指标上显著优于现有方法，开创了多模态逼真风格迁移的强大灵活范式。
英文摘要：
Photorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image while strictly preserving its structural integrity. However, existing deep learning-based methods inherently suffer from semantic entanglement caused by pre-trained image encoders, leading to unnatural spatial distortions. Moreover, current pixel-level mapping paradigms often ignore color gamut topology, resulting in color banding, while also lacking the multimodal capability for intuitive text-driven control. To address these bottlenecks, we propose StatLUT, an innovative multimodal framework for 3D LUT generation. First, we bypass traditional encoders and introduce a Lab-Extractor to derive spatially-agnostic statistical features, fundamentally decoupling color distributions from structural semantics to ensure artifact-free rendering. Second, we formulate LUT generation as a Transformer-based Seq2Seq translation task, utilizing a Multi-dimensional Residual Mapper (MR-Mapper) to predict topologically smooth 3D LUTs. Finally, to break the single-modal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from natural language prompts, enabling flexible text-driven color grading. Extensive experiments on standard benchmarks demonstrate that StatLUT significantly outperforms state-of-the-art methods in both visual quality and quantitative metrics, pioneering a highly robust and flexible paradigm for multimodal photorealistic style transfer.
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