NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
Michael Fischer Zhengqin Li Thu Nguyen-Phuoc Aljaz Bozic Zhao Dong Carl Marshall Tobias Ritschel
A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry
and appearance of a scene. We here ask the question whether we can transfer the
appearance from a source NeRF onto a target 3D geometry in a semantically
meaningful way, such that the resulting new NeRF retains the target geometry
but has an appearance that is an analogy to the source NeRF. To this end, we
generalize classic image analogies from 2D images to NeRFs. We leverage
correspondence transfer along semantic affinity that is driven by semantic
features from large, pre-trained 2D image models to achieve multi-view
consistent appearance transfer. Our method allows exploring the mix-and-match
product space of 3D geometry and appearance. We show that our method
outperforms traditional stylization-based methods and that a large majority of
users prefer our method over several typical baselines.