# DreamFusion: Text-to-3D using 2D Diffusion

**Authors:** Ben Poole, Ajay Jain, Jonathan T. Barron, Ben Mildenhall  
**arXiv:** [2209.14988](https://arxiv.org/abs/2209.14988)  
**Date:** 2022-09-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2209.14988v1/

## Key points

- Authors Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall explore text-to-3D synthesis using pretrained image diffusion models.
- The study introduces a novel method leveraging a 2D text-to-image diffusion model as a prior for optimizing a parametric image generator for 3D synthesis.
- A loss function based on probability density distillation is introduced to refine a randomly initialized 3D model (Neural Radiance Field) through gradient descent.
- The resulting 3D model can be visualized from various perspectives, illuminated by different light sources, and integrated into diverse 3D environments without the need for specific training data in the 3D domain or modifications to existing image diffusion models.

## Summary

In their paper titled "DreamFusion: Text-to-3D using 2D Diffusion," authors Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall explore the realm of text-to-image synthesis in a three-dimensional space. The study showcases the efficacy of pretrained image diffusion models as effective priors for facilitating text-to-3D synthesis without extensive labeled datasets or specialized denoising techniques. <br/>

Recent advancements in text-to-image synthesis have predominantly relied on diffusion models trained on vast amounts of image-text pairs. However, extending this approach to 3D synthesis presents challenges such as the lack of large-scale labeled 3D datasets and efficient denoising architectures for 3D data. To overcome these obstacles, the authors propose a novel method that leverages a pretrained 2D text-to-image diffusion model for text-to-3D synthesis.<br/>

They introduce a loss function based on probability density distillation, enabling the utilization of the 2D diffusion model as a prior for optimizing a parametric image generator. Through a DeepDream-like procedure, they employ gradient descent to refine a randomly initialized 3D model known as a Neural Radiance Field (NeRF). This optimization process aims to minimize the loss incurred when rendering the 3D model from various angles in its 2D projections.<br/>

The resulting 3D model generated from textual input can be visualized from any perspective, illuminated by different light sources, or seamlessly integrated into diverse 3D environments. Remarkably, this approach eliminates the need for specific training data in the 3D domain and requires no modifications to the existing image diffusion model.<br/>

 is a challenging task that has gained significant attention in recent years. , which are commonly used in text-to-image synthesis, have shown promising results. However, applying these models to 3D data poses unique challenges due to the lack of large-scale labeled datasets and efficient denoising techniques. In their paper, "DreamFusion," Poole et al. propose a novel approach that utilizes a  as a prior for optimizing a parametric image generator for . This method involves using gradient descent to refine a randomly initialized 3D model known as a , minimizing the loss incurred when rendering the 3D model from various angles in its 2D projections.<br/>

The resulting 3D model can be visualized from any perspective, illuminated by different light sources, or seamlessly integrated into diverse 3D environments. What makes this approach remarkable is that it eliminates the need for specific training data in the 3D domain and requires no modifications to the existing image diffusion model. This study showcases how pretrained image diffusion models can serve as effective priors for facilitating , bridging the gap between text-based descriptions and immersive three-dimensional visualizations through the adaptation of established techniques in image synthesis to the realm of .