[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84336-en":3,"doc-seo-84336-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84336,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer","Photorealistic Style Transfer transfers color and tonal style from a reference to a content image while preserving spatial structure. Existing deep-learning approaches entangle semantics from pre-trained encoders, causing spatial distortions; pixel-level LUT mapping also overlooks color gamut topology, leading to color banding, and lacks multimodal text control. StatLUT introduces a Lab-Extractor for spatiallyagnostic statistical features, a Transformer Seq2Seq LUT generator with an MR-Mapper for topology-smooth LUTs, and an H-Diffuser diffusion transformer to synthesize features from natural language prompts, achieving robust multimodal quality gains.","arXiv :2607 .08227v 1 [ cs .CV] 9 Jul 2026  \nMultimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer  \nYifan Wang, Zhixiang Hao, Yu Wang Congchao Zhu  \nHonor Device Co., Ltd.  \n[wyf141060@163.com](wyf141060@163.com) , {haozhixiang, wangyu24, [zhucongchao}@honor.com](zhucongchao}@honor.com)  \nAbstract  \nPhotorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image while strictly preserving its structural integrity.  \nHowever, 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 spatiallyagnostic 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 Multidimensional 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.  \n1 Introduction  \nPhotorealistic Style Transfer (PST) aims to faithfully transfer the color and tonal style of a reference image to a content image while strictly preserving the original spatial structure. Unlike artistic style transfer[1–5], PST demands visual realism and structural fidelity, precluding unnatural artifacts or texture distortions during non-linear color mapping.  \nAlthough deep learning has advanced PST, adhering to this “artifact-free” baseline remains challenging. Current mainstream methods widely adopt pre-trained encoder-decoder architectures[6–8] . However, these models naturally extract high-level semantic features, creating a fundamental mechanistic mismatch with the PST task, which inherently relies on low-level color distributions. This semantic entanglement allows local structural semantics to interfere with color mapping, leading to spatial distortions. Furthermore, their massive memory consumption constitutes a prohibitive bottleneck for high-resolution (e.g., 4K/8K) deployment[9, 10] .  \nTo overcome these barriers, recent research has shifted towards a pixel-level mapping paradigm[9– 11], with 3D lookup tables (LUTs) emerging as the standard due to their non-linear capacity and low computational cost[10] . Nevertheless, existing LUT generation mechanisms exhibit significant  \n∗ Corresponding author.  \nPreprint.  \nlimitations: 1) traditional blending paradigms[12, 13] restrict representations to a linear subspace, hindering out-of-distribution generalization; 2) recent generative paradigms[14] fail to completely decouple semantics from color, risking texture artifacts; 3) point-wise mapping approaches[9] ignore the topology between adjacent color bins, causing color banding and inter-frame flickering in videos. Moreover, they lack multimodal capabilities for intuitive text-driven color control.  \nTo address these limitations, we propose StatLUT, a novel multimodal photorealistic style transfer and color generation framework. In summary, our main contributions are as follows:  \n• Bypassing traditional image encoders, we utilize multi-dimensional l","cbCaiedlNaqruAeC","https://ap.wps.com/l/cbCaiedlNaqruAeC","pdf",32495874,1,17,"English","en",105,"# Introduction\n## Contributions\n# Related Work\n## Feature-Encoding-based Photorealistic Style Transfer\n## Pixel-Level Mapping and 3D LUT Generation","[{\"question\":\"What problem does StatLUT address in photorealistic style transfer?\",\"answer\":\"It targets semantic entanglement from pre-trained encoders that causes spatial distortions, as well as limitations of LUT mapping that ignore color gamut topology and lack multimodal text-driven control.\"},{\"question\":\"How does StatLUT decouple color distributions from structural semantics?\",\"answer\":\"It bypasses traditional encoders and uses a Lab-Extractor to derive spatiallyagnostic low-level statistical features, serving as explicit color priors to block semantic interference.\"},{\"question\":\"How does StatLUT enable text-driven color grading without a reference image?\",\"answer\":\"It introduces an H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from natural language prompts, breaking the single-modal barrier.\"}]",1784194909,43,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"multimodal-3d-lut-generation-via-statlut-with-statistical-features-for-photorealistic-style-transfer","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/multimodal-3d-lut-generation-via-statlut-with-statistical-features-for-photorealistic-style-transfer/84336/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does StatLUT address in photorealistic style transfer?","Question",{"text":75,"@type":76},"It targets semantic entanglement from pre-trained encoders that causes spatial distortions, as well as limitations of LUT mapping that ignore color gamut topology and lack multimodal text-driven control.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does StatLUT decouple color distributions from structural semantics?",{"text":80,"@type":76},"It bypasses traditional encoders and uses a Lab-Extractor to derive spatiallyagnostic low-level statistical features, serving as explicit color priors to block semantic interference.",{"name":82,"@type":73,"acceptedAnswer":83},"How does StatLUT enable text-driven color grading without a reference image?",{"text":84,"@type":76},"It introduces an H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from natural language prompts, breaking the single-modal barrier.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]