Summary of Coarf: Controllable 3d Artistic Style Transfer For Radiance Fields, by Deheng Zhang et al.
CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields
by Deheng Zhang, Clara Fernandez-Labrador, Christopher Schroers
First submitted to arxiv on: 23 Apr 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 Controllable Artistic Radiance Fields (CoARF), a novel algorithm for controllable 3D scene stylization. Building on recent approaches such as ARF, CoARF enables fine-grained control over the resulting scenes by allowing users to specify objects, compositional styles, and semantic-aware styles. The approach uses segmentation masks with different label-dependent loss functions to achieve controllability, while a proposed nearest neighbor matching algorithm improves style transfer quality. Experimental results demonstrate superior performance of CoARF in terms of user-specified controllability and precision feature matching. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoARF is a new way to make 3D scenes look like certain styles or objects. Right now, creating these scenes can be hard and requires special knowledge. This paper makes it easier by allowing users to control what parts of the scene change and how they change. The method uses special masks to help achieve this control. It also has a new way to match features in the scene to improve the results. The tests show that CoARF works better than other methods at making scenes look like what you want them to. |
Keywords
» Artificial intelligence » Nearest neighbor » Precision » Style transfer
