Summary of Fprf: Feed-forward Photorealistic Style Transfer Of Large-scale 3d Neural Radiance Fields, by Geonu Kim et al.
FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields
by GeonU Kim, Kim Youwang, Tae-Hyun Oh
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces FPRF, a novel feed-forward photorealistic style transfer method for large-scale 3D neural radiance fields. The approach enables the stylization of large-scale 3D scenes with multiple style reference images without additional optimization while maintaining multi-view appearance consistency. Unlike prior arts that required tedious per-style/-scene optimization and were limited to small-scale 3D scenes, FPRF efficiently stylizes large-scale scenes by incorporating a style-decomposed 3D neural radiance field, which inherits the feed-forward stylization machinery from AdaIN, supporting arbitrary style reference images. Additionally, FPRF supports multi-reference stylization with semantic correspondence matching and local AdaIN, offering diverse user control for 3D scene styles. The method also preserves multi-view consistency by applying semantic matching and style transfer processes directly onto queried features in 3D space. Experimental results demonstrate the favorable photorealistic quality of FPRF’s 3D scene stylization for large-scale scenes with diverse reference images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to change the look of big 3D scenes. The approach, called FPRF, lets you give those scenes many different styles without having to do extra work. This means you can take a big 3D scene and make it look like a different place or time period just by giving it some style reference images. The method is special because it works well with large-scale scenes and multiple styles, which hasn’t been done before. |
Keywords
» Artificial intelligence » Optimization » Style transfer
