FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields
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Abstract (arXiv)
We present FPRF, a feed-forward photorealistic style transfer method for large-scale 3D neural radiance fields. FPRF stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization while preserving multi-view appearance consistency. Prior arts required tedious per-style/-scene optimization and were limited to small-scale 3D scenes. FPRF efficiently stylizes large-scale 3D scenes by introducing a style-decomposed 3D neural radiance field, which inherits AdaIN's feed-forward stylization machinery, supporting arbitrary style reference images. Furthermore, FPRF supports multi-reference stylization with the semantic correspondence matching and local AdaIN, which adds diverse user control for 3D scene styles. FPRF also preserves multi-view consistency by applying semantic matching and style transfer processes directly onto queried features in 3D space. In experiments, we demonstrate that FPRF achieves favorable photorealistic quality 3D scene stylization for large-scale scenes with diverse reference images. Project page: https://kim-geonu.github.io/FPRF/
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 10 Jan 2024.
Builds on
27 of 40 references.
References · 27
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
- 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2023K-Planes: Explicit Radiance Fields in Space, Time, and Appearancecited by 742
- 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 720
15 more
- 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
- 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 413
- 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 396
- 2018Learning Blind Video Temporal Consistencycited by 247
- 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
- 2022Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representationscited by 195
- 2022StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learningcited by 140
- 2021Stylizing 3D Scene via Implicit Representation and HyperNetworkcited by 103
- 2022UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scenecited by 47
- 2022HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fieldscited by 40
- 2020Neural Pose Transfer by Spatially Adaptive Instance Normalizationcited by 37
- 2021PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residualscited by 30
- 2023Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylizationcited by 14
- 20203DSNet: Unsupervised Shape-to-Shape 3D Style Transfercited by 9
- 2023Modernizing Old Photos Using Multiple References via Photorealistic Style Transfercited by 4
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Citing papers per year: 2025: 5; 2026: 4.
9 papers.
- 2025Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registrationcited by 50
- 2025ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfercited by 2
- 2026SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selectioncited by 2
- 2025FPGS: Feed-Forward Semantic-aware Photorealistic Style Transfer of Large-Scale Gaussian Splattingcited by 1
- 2025Improved 3D Scene Stylization via Text-Guided Generative Image Editing with Region-Based Controlcited by 1
- 2025PAVAS: Physics-Aware Video-to-Audio Synthesiscited by 1
- 2026Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devicescited by 1
- 2026Capacity-Controlled Multi-View Stylization of 3D Gaussian Splattingcited by 0
- 2026OrnaStyler: Ornament-Aware Latent Editing for Content-Preserving 3D Stylizationcited by 0
- 2026OrnaStyler: Ornament-Aware Latent Editing for Content-Preserving 3D Stylization30 Aug 2026
- 2026Capacity-Controlled Multi-View Stylization of 3D Gaussian Splatting25 Jun 2026
- 2026Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices3 Apr 2026
- 2026SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection6 Jan 2026
- 2025PAVAS: Physics-Aware Video-to-Audio Synthesis9 Dec 2025
- 2025Improved 3D Scene Stylization via Text-Guided Generative Image Editing with Region-Based Control4 Sep 2025
- 2025ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer19 Mar 2025
- 2025FPGS: Feed-Forward Semantic-aware Photorealistic Style Transfer of Large-Scale Gaussian Splatting11 Mar 2025
- 2025Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration23 Feb 2025