# Magic3D: High-Resolution Text-to-3D Content Creation

**Authors:** Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, Tsung-Yi Lin  
**arXiv:** [2211.10440](https://arxiv.org/abs/2211.10440)  
**Date:** 2022-11-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2211.10440v1/

## Key points

- Magic3D is a novel method that addresses the shortcomings of existing text-to-3D synthesis models.
- DreamFusion, the current state-of-the-art model, has slow optimization and low-resolution image space supervision, resulting in low-quality 3D models with long processing times.
- The authors propose a two-stage optimization framework to overcome these limitations.
- In the first stage, they use a low-resolution diffusion prior and a sparse 3D hash grid structure to obtain a coarse model.
- The second stage involves optimizing a textured 3D mesh model using an efficient differentiable renderer and high-resolution latent diffusion model.
- Magic3D creates high-quality 3D mesh models in just 40 minutes, twice as fast as DreamFusion's reported average time of 1.5 hours.
- User studies show that 61.7% of raters prefer Magic3D over DreamFusion due to its faster processing time and better quality results.
- Magic3D offers new ways to control 3D synthesis through prompt-based editing and various creative controls on the generated models.
- It aims to democratize 3D content creation by providing unprecedented control in crafting desired objects with text prompts and reference images while reducing computation time.

## Summary

The authors present Magic3D, a novel method that addresses the shortcomings of existing text-to-3D synthesis models. The current state-of-the-art model, DreamFusion, suffers from slow optimization of Neural Radiance Fields (NeRF) and low-resolution image space supervision on NeRF. This results in low-quality 3D models with long processing times. To overcome these limitations, the authors propose a two-stage optimization framework. In the first stage, they use a low-resolution diffusion prior and accelerate the optimization process with a sparse 3D hash grid structure to obtain a coarse model. This serves as the initialization for the second stage where they further optimize a textured 3D mesh model using an efficient differentiable renderer and high-resolution latent diffusion model. The proposed method, Magic3D, is able to create high-quality 3D mesh models in just 40 minutes - twice as fast as DreamFusion's reported average time of 1.5 hours. Additionally, it achieves higher resolution results compared to DreamFusion. User studies show that 61.7% of raters prefer Magic3D over DreamFusion due to its faster processing time and better quality results. Moreover, Magic3D offers new ways to control 3D synthesis through prompt-based editing and various creative controls on the generated models. This opens up new possibilities for creative applications and brings us closer to democratizing 3D content creation. In conclusion, this paper introduces Magic3D as a fast and high-quality text-to-3D generation framework that overcomes the limitations of existing models. It offers unprecedented control in crafting desired 3D objects with text prompts and reference images while significantly reducing computation time. The authors hope that Magic3D will democratize 3D synthesis and unleash creativity in 3D content creation across various domains.