Summary of Scaledreamer: Scalable Text-to-3d Synthesis with Asynchronous Score Distillation, by Zhiyuan Ma et al.
ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation
by Zhiyuan Ma, Yuxiang Wei, Yabin Zhang, Xiangyu Zhu, Zhen Lei, Lei Zhang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 proposes Asynchronous Score Distillation (ASD), a novel method for synthesizing 3D contents without paired text-3D training data. By leveraging text-to-image diffusion priors and score distillation, ASD can generate high-quality 3D contents in seconds, outperforming existing methods like Variational Score Distillation. The key innovation is shifting the diffusion timestep to earlier ones, which minimizes noise prediction errors while preserving the comprehension capability of pre-trained models. ASD demonstrates its effectiveness across various 2D diffusion models and text-to-3D generators, showcasing its potential for stable training and high-quality content synthesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create 3D objects from text descriptions without needing lots of examples to learn. The old way was slow and didn’t work well for many different prompts. The new method, called Asynchronous Score Distillation (ASD), is faster and better at handling a large number of prompts. ASD uses special computer programs that can generate images and 3D objects from text descriptions. It’s like having a magic box that can create anything you describe! |
Keywords
» Artificial intelligence » Diffusion » Distillation
