English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Neural Radiance Fields (NeRFs) have recently emerged as a popular option forphoto-realistic object capture due to their ability to faithfully capturehigh-fidelity volumetric content even from handheld video input. Although muchresearch has been devoted to efficient optimization leading to real-timetraining and rendering, options for interactive editing NeRFs remain limited.We present a very simple but effective neural network architecture that is fastand efficient while maintaining a low memory footprint. This architecture canbe incrementally guided through user-friendly image-based edits. Ourrepresentation allows straightforward object selection via semantic featuredistillation at the training stage. More importantly, we propose a local3D-aware image context to facilitate view-consistent image editing that canthen be distilled into fine-tuned NeRFs, via geometric and appearanceadjustments. We evaluate our setup on a variety of examples to demonstrateappearance and geometric edits and report 10-30x speedup over concurrent workfocusing on text-guided NeRF editing. Video results can be seen on our projectwebpage at https://proteusnerf.github.io.

ProteusNeRF: Fast Lightweight NeRF Editing using 3D-Aware Image Context