Summary of An Object Is Worth 64×64 Pixels: Generating 3d Object Via Image Diffusion, by Xingguang Yan et al.
An Object is Worth 64×64 Pixels: Generating 3D Object via Image Diffusion
by Xingguang Yan, Han-Hung Lee, Ziyu Wan, Angel X. Chang
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach, “Object Images,” generates realistic 3D models with UV maps by representing complex 3D shapes as 2D images. This 64×64 pixel image encapsulates surface geometry, appearance, and patch structures, allowing for the use of image generation models like Diffusion Transformers for 3D shape generation. The method addresses geometric and semantic irregularities in polygonal meshes, achieving point cloud FID comparable to recent 3D generative models on the ABO dataset while supporting PBR material generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to create realistic 3D models with textures by treating them as 2D images. This helps fix problems with complex shapes and makes it possible to use image-making AI models for 3D creation. Our method does this by combining geometry, appearance, and texture patterns into a single image. The result is 3D shapes that look good and can be used in real-world applications like gaming or virtual reality. |
Keywords
» Artificial intelligence » Diffusion » Image generation
