Summary of Dreampolisher: Towards High-quality Text-to-3d Generation Via Geometric Diffusion, by Yuanze Lin et al.
DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric Diffusion
by Yuanze Lin, Ronald Clark, Philip Torr
First submitted to arxiv on: 25 Mar 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 A novel text-to-3D generation method called DreamPolisher is introduced, which learns cross-view consistency and intricate detail from textual descriptions. The method addresses the limitations of recent progress in this area by ensuring view-consistency and textural richness. It uses a two-stage approach that combines Gaussian Splatting with geometric guidance to generate realistic 3D assets. The paper presents empirical evaluations across various object categories, demonstrating the effectiveness of DreamPolisher in generating consistent and realistic 3D objects aligned with textual instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DreamPolisher is a new way to turn text into 3D images. It makes sure that different views of an object look similar and that the image has lots of details. This is important because previous methods didn’t always get it right. The method uses two stages: first, it generates a rough 3D shape, then refines it to make it more realistic. The result is a 3D image that looks like what you would see if you were looking at the object in real life. |
