FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D
  Neural Radiance Fields
GeonU Kim Kim Youwang Tae-Hyun Oh
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/