DreamFusion

DreamFusion is an innovative platform that allows users to generate high-quality 3D objects from simple text captions. Conventional 3D synthesis methods require large-scale datasets of labeled 3D assets and efficient architectures for denoising 3D data, which are both time-consuming and resource-intensive tasks. However, DreamFusion circumvents these limitations by leveraging a pretrained 2D text-to-image diffusion model as a prior for optimization of a 3D object.

DreamFusion's approach to generating 3D objects from text captions is based on recent breakthroughs in text-to-image synthesis driven by diffusion models. However, adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D assets and efficient architectures for denoising 3D data, which currently do not exist. That's where DreamFusion's unique approach comes in.

The platform uses a pretrained text-to-image diffusion model and adds a loss function based on probability density distillation to optimize a randomly-initialized 3D model (Neural Radiance Field or NeRF) via gradient descent. The resulting 3D object can be viewed from any angle, relit by any illumination, or composited into any 3D environment. DreamFusion's approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors.

The generated 3D objects are represented as NeRF models with high-fidelity appearance, depth, and normals. DreamFusion's efficient and effective approach allows users to generate a wide range of objects with diverse captions such as "a squirrel wearing a medieval suit of armor" or "an elegant ballgown". Users can also search through hundreds of generated assets in the full gallery.

DreamFusion also allows for easy integration of generated NeRF models into 3D renderers or modeling software with mesh exports using the marching cubes algorithm. This enables users to take advantage of DreamFusion's advanced 3D object generation capabilities while seamlessly integrating them into their existing workflows.

To generate the 3D objects, DreamFusion uses a text-to-image generative model called Imagen to optimize a 3D scene. The platform proposes Score Distillation Sampling (SDS) as a way to generate samples from a diffusion model by optimizing a loss function. SDS allows DreamFusion to optimize samples in an arbitrary parameter space, such as a 3D space, as long as it can map back to images differentiably. The platform uses a 3D scene parameterization similar to NeRFs to define this differentiable mapping. SDS alone produces reasonable scene appearance, but DreamFusion adds additional regularizers and optimization strategies to improve geometry.

In conclusion, DreamFusion is a unique 3D object generation platform that leverages a pretrained text-to-image diffusion model and loss function based on probability density distillation to generate high-quality 3D objects from simple text captions. The platform's approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors. DreamFusion's efficient and effective approach allows users to generate a wide range of high-quality 3D objects that can be seamlessly integrated into their existing workflows.