← 研踪 PaperTrailarXiv:2607.08227 · v2
StatLUT: Statistical Feature-Driven Multimodal 3DLUT Generation for Photorealistic Style Transfer
首次提交 2026-07-09 16:23
版本更新 2026-11-03 11:15
雷达观测 2026-11-04 23:40
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 precise, tone-specific 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 unimodal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from tone-descriptive text prompts, enabling controllable and robust 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 versatile paradigm for multimodal photorealistic style transfer.
作者备注：Accepted at NeurIPS 2026. Camera-ready version
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-03 11:15 · 最新观测版本
StatLUT: Statistical Feature-Driven Multimodal 3DLUT Generation for Photorealistic Style Transfer
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 precise, tone-specific 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 unimodal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from tone-descriptive text prompts, enabling controllable and robust 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 versatile paradigm for multimodal photorealistic style transfer.
查看此版本解读 →查看此版本原文 ↗