  Neural Radiance Fields (NeRFs) have recently emerged as a popular option for
photo-realistic object capture due to their ability to faithfully capture
high-fidelity volumetric content even from handheld video input. Although much
research has been devoted to efficient optimization leading to real-time
training and rendering, options for interactive editing NeRFs remain limited.
We present a very simple but effective neural network architecture that is fast
and efficient while maintaining a low memory footprint. This architecture can
be incrementally guided through user-friendly image-based edits. Our
representation allows straightforward object selection via semantic feature
distillation at the training stage. More importantly, we propose a local
3D-aware image context to facilitate view-consistent image editing that can
then be distilled into fine-tuned NeRFs, via geometric and appearance
adjustments. We evaluate our setup on a variety of examples to demonstrate
appearance and geometric edits and report 10-30x speedup over concurrent work
focusing on text-guided NeRF editing. Video results can be seen on our project
webpage at https://proteusnerf.github.io.
