Summary of Supergaussian: Repurposing Video Models For 3d Super Resolution, by Yuan Shen et al.
SuperGaussian: Repurposing Video Models for 3D Super Resolution
by Yuan Shen, Duygu Ceylan, Paul Guerrero, Zexiang Xu, Niloy J. Mitra, Shenlong Wang, Anna Frühstück
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to upscaling coarse 3D models by adding geometric and appearance details, leveraging existing video upsampling models. The proposed method, called SuperGaussian, repurposes pre-trained video models for 3D super-resolution, sidestepping the need for large repositories of high-quality 3D training data. By combining video upsampling with 3D consolidation, SuperGaussian produces high-quality, 3D-consistent results that are category-agnostic and can be easily incorporated into existing workflows. The authors evaluate their method on a variety of 3D inputs, demonstrating significant improvements in the fidelity of final 3D models. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make 3D models look better by adding more details. It shows how to use video models that are already good at making images sharper and apply them to 3D models too. The method is simple and can be used with many different types of 3D models, making it useful for people working in this field. |
Keywords
* Artificial intelligence * Super resolution