DreamFusion vs Text-To-4D

Discover DreamFusion, the revolutionary platform that combines the power of text-to-3D synthesis with cutting-edge 2D diffusion technology. Developed by a collaborative effort from Google Research and UC Berkeley, DreamFusion allows you to effortlessly transform textual descriptions into vivid 3D models without the need for large-scale datasets or complex 3D denoising architectures.

Utilizing a state-of-the-art 2D text-to-image diffusion model as a foundational prior, DreamFusion introduces a novel loss based on probability density distillation. This innovative approach enables seamless optimization of Neural Radiance Fields (NeRF) through gradient descent, producing high-quality 3D renderings from various angles with low loss. The resulting 3D models offer remarkable features such as viewability from any perspective, relightable textures under various illumination conditions, and the ability to be composited into diverse 3D environments seamlessly.

DreamFusion emphasizes ease of use and accessibility, requiring no 3D training data or tweaks to the existing image diffusion model. By harnessing the pre-trained image diffusion models as a robust prior, DreamFusion demonstrates the power of these models beyond mere 2D applications. Explore DreamFusion's gallery to witness the range of objects and scenes it can generate, and take the exciting step to generate your very own 3D model from text today!

Meta present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency by querying a Text-to-Video (T2V) diffusion-based model.

The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment. MAV3D does not require any 3D or 4D data and the T2V model is trained only on Text-Image pairs and unlabeled videos.