Text-to-3D: Google has successfully developed a neural network that generates intricate 3D models directly from written descriptions.

In Brief

Text-to-3D This neural network has the capability to turn textual input into 3D models.

DreamFusion enhances the creation of 3D scenes by utilizing the powerful Imagen text-to-image model.

A diffusion model that operates in 2D can also facilitate the synthesis of images from text.

Google created a neural network This cutting-edge technology can produce 3D models from simple text prompts. Notably, the most complex parts did not even need to be explicitly trained. The foundation for this process is the Imagen model, which supports Text-to-3D.

What are the key points you should understand about DreamFusion ?

Recent improvements in synthesizing images from text stem from diffusion models trained on extensive datasets of image-text pairs. To apply this methodology to 3D synthesis, we will require large-scale labeled 3D asset datasets and effective 3D data denoising structures, both of which are presently lacking. Our research tackles these challenges by conducting text-to-3D synthesis with a pretrained 2D model. We introduce a loss function based on probability density distillation, allowing a 2D diffusion model to serve as a prior for refining a parametric representation. By employing this loss function, we can utilize gradient descent to enhance a randomly initialized 3D model (specifically, a Neural Radiance Field or NeRF) until its 2D renderings, viewed from any random angle, achieve the lowest possible loss. text-to-image diffusion The resulting 3D model, crafted based on the provided text, can be explored from multiple angles, illuminated with various lighting options, and seamlessly integrated into any virtual environment. This method does not rely on any 3D training datasets and does not require alterations to the original design, highlighting the power of employing pretrained image diffusion models as a foundational step. picture generator DreamFusion generates 3D models that can be re-lit while maintaining high visual fidelity, depth, and normal details, all based on textual input. These objects are depicted as a Neural Radiance Field and are built upon a pretrained framework.

Prompt: an image of a squirrel clad in medieval armor skillfully playing a saxophone. image diffusion model Prompt: an elegant image of a squirrel dressed in a stunning ballgown, seated at a pottery wheel, skillfully crafting a clay bowl.

Prompt: a highly detailed metallic sculpture depicting a squirrel in a purple hoodie, confidently riding a motorcycle. text-to-image diffusion prior like Imagen being used.

Examples of Generated 3D From Text

Prompt: an intricately carved wooden representation of a squirrel in medieval armor wielding a katana.

Assembling various elements to create a cohesive scene.

DreamFusion enhances a 3D scene based on descriptive captions utilizing the Imagen text-to-image generative model. It proposes an innovative approach called Score Distillation Sampling (SDS), which suggests optimizing a loss function to generate samples from a diffusion model. By implementing SDS, we can adapt the sample output for fine-tuning in any parameter space, including 3D dimensions, as long as we have a viable mapping strategy to revert to images. To establish this differentiable mapping, the 3D scene parameterization resembles that of Neural Radiance Fields (NeRFs). While SDS alone can yield a visually acceptable scene, DreamFusion elevates the geometry utilizing additional regularizers and optimization methodologies. The resulting NeRFs are coherent, possess exceptional normals, surface geometry, depth, and can be relit using a Lambertian shading approach.

Meta has revealed an AI system capable of generating videos directly from text.

Here are 6 complimentary AI prompt generators, tools, and assistants that artists genuinely find useful in 2022.

How does it work?

Discover the leading 50 text-to-image prompts for AI art generators like Midjourney and DALL-E.

Read related articles:

Tags:

Disclaimer

In line with the Trust Project guidelines DeFAI must address the challenges of cross-chain compatibility to realize its full potential.