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/

翻译：我们提出FPRF，一种面向大规模3D神经辐射场的前馈式真实感风格迁移方法。FPRF无需额外优化即可利用任意多张风格参考图像对大规模3D场景进行风格化处理，同时保持多视角外观一致性。现有技术需要针对每种风格/场景进行繁琐的优化，且局限于小规模3D场景。FPRF通过引入风格解耦的3D神经辐射场，继承AdaIN的前馈式风格化机制，高效实现大规模3D场景风格化，支持任意风格参考图像。此外，FPRF通过语义对应匹配和局部AdaIN支持多参考风格化，为3D场景风格提供多样化的用户控制。通过直接在3D空间中对查询特征施加语义匹配和风格迁移过程，FPRF保持了多视角一致性。实验表明，FPRF在处理大规模场景与多样参考图像时，能够生成具有竞争力的真实感风格化3D场景。项目页面：https://kim-geonu.github.io/FPRF/