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We present FPRF, a feed-forward photorealistic style transfer method forlarge-scale 3D neural radiance fields. FPRF stylizes large-scale 3D scenes witharbitrary, multiple style reference images without additional optimizationwhile preserving multi-view appearance consistency. Prior arts required tediousper-style/-scene optimization and were limited to small-scale 3D scenes. FPRFefficiently stylizes large-scale 3D scenes by introducing a style-decomposed 3Dneural radiance field, which inherits AdaIN's feed-forward stylizationmachinery, supporting arbitrary style reference images. Furthermore, FPRFsupports multi-reference stylization with the semantic correspondence matchingand local AdaIN, which adds diverse user control for 3D scene styles. FPRF alsopreserves multi-view consistency by applying semantic matching and styletransfer processes directly onto queried features in 3D space. In experiments,we demonstrate that FPRF achieves favorable photorealistic quality 3D scenestylization for large-scale scenes with diverse reference images. Project page:https://kim-geonu.github.io/FPRF/

FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields