Summary of Latte3d: Large-scale Amortized Text-to-enhanced3d Synthesis, by Kevin Xie et al.
LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis
by Kevin Xie, Jonathan Lorraine, Tianshi Cao, Jun Gao, James Lucas, Antonio Torralba, Sanja Fidler, Xiaohui Zeng
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel approach called LATTE3D that enables fast and high-quality text-to-3D generation while addressing the limitations of existing methods. The authors note that current approaches require up to an hour per prompt for optimization, but LATTE3D optimizes multiple prompts simultaneously, leveraging 3D data during training through diffusion priors, shape regularization, and model initialization. This scalable architecture achieves robustness to diverse and complex training prompts, allowing for highly detailed textured meshes in a single forward pass. The authors demonstrate the efficacy of LATTE3D by generating 3D objects in 400ms, with potential for further enhancement at test time. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary LATTE3D is a new way to turn text into 3D objects quickly and accurately. Right now, it takes a lot of time and effort to get good results from existing methods. The team behind LATTE3D wanted to fix this by creating a system that can handle lots of prompts at once and still produce great results. They did this by using special tricks during training, like looking at 3D data in a new way and using certain rules to keep the model on track. This helped LATTE3D become really good at generating detailed textures and shapes from text prompts. |
Keywords
* Artificial intelligence * Diffusion * Optimization * Prompt * Regularization