NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
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.
65cc31c628f6e6716cb60009
Michael Fischer
claimed_verified
2024-02-15T09:44:35.475Z
65cdd1f42f25d72f998744ed
/avatars/29030f71078de3a8f6aee541eb59af44.svg
Michael Fischer
user
mfai
65cc31c628f6e6716cb6000a
Zhengqin Li
admin_assigned
2024-02-14T12:47:11.769Z
64ff5b2569219ce3e47893f1
/avatars/36bfb3de85488c0efa75d741eac9c40e.svg
Li
user
Zhengqin
65cc31c628f6e6716cb6000b
Thu Nguyen-Phuoc
admin_assigned
2024-02-14T12:47:18.106Z
6304b663c2f4f2d4929ad200
/avatars/805d4dab512fc53d8bd6bcb60db3821b.svg
Thu Nguyen-Phuoc
user
TNP23
65cc31c628f6e6716cb6000c
Aljaz Bozic
65cc31c628f6e6716cb6000d
Zhao Dong
65cc31c628f6e6716cb6000e
Carl Marshall
65cc31c628f6e6716cb6000f
Tobias Ritschel
NeRF Analogies: Example-Based Visual Attribute Transfer for NeRFs
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.
The method enables transferring appearance from a source NeRF to target 3D geometry using semantic features from pre-trained image models, achieving better results than traditional stylization methods.
Neural Radiance Field appearance transfer semantic affinity pre-trained image models multi-view consistency