DreamFusion

What is DreamFusion

DreamFusion is a tool that utilizes a pretrained 2D text-to-image diffusion model to generate relightable 3D objects with high-fidelity appearance, depth, and normals based on given captions. It optimizes a randomly-initialized 3D model through a DeepDream-like procedure to achieve low loss in its 2D renderings. The resulting 3D models can be viewed from different angles, relit by various illuminations, and integrated into any 3D environment without the need for 3D training data or modifications to the image diffusion model.

Key Features of DreamFusion

- Text-to-3D Synthesis: Utilizes a pretrained 2D text-to-image diffusion model to create 3D models from text prompts.

- No 3D Training Data Required: Circumvents the need for large-scale 3D asset datasets by leveraging existing 2D models.

- Probability Density Distillation Loss: Introduces a unique loss function to enable the use of 2D diffusion models as priors for 3D model optimization.

- Neural Radiance Field (NeRF): Represents 3D objects using NeRF, facilitating high-fidelity renderings from various angles.

- Versatile 3D Objects: Produces relightable objects with high-quality appearance, depth, and normals.

- Diverse Generation: Generates 3D models from a wide range of text captions, allowing for creative and detailed outputs.

- Mesh Exports: Exports NeRF models to meshes using the marching cubes algorithm, simplifying integration into 3D renderers and modeling software.

- Score Distillation Sampling (SDS): Introduces SDS for generating samples from a diffusion model by optimizing a loss function in arbitrary parameter spaces.

- Enhanced Geometry: Uses additional regularizers and optimization strategies to improve the geometry and coherence of the 3D models.

- Gallery of Generated Assets: Provides a comprehensive gallery of hundreds of generated 3D assets for browsing and downloading.

- Flexible Scene Integration: Models can be composed into diverse scenes, allowing for flexible and creative usage scenarios.