Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer

• URL: http://arxiv.org/abs/2607.08227v1
• Date: Thu, 09 Jul 2026 08:23:04 GMT
• Title: Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer
• Abstract summary: Photorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image.<n>Existing deep learning-based methods inherently suffer from semantic entanglement caused by pre-trained image encoders.<n>We propose StatLUT, an innovative multimodal framework for 3D LUT generation.
• Score: 6.827125120104924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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