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What is RODIN?

This groundbreaking model for 3D avatar diffusion represents a sophisticated artificial intelligence system aimed at producing highly intricate digital avatars in three-dimensional space. Users are offered the opportunity to examine these avatars from various perspectives, achieving an extraordinary standard of visual quality. By simplifying the traditionally complex practice of 3D modeling, this innovative model opens doors to fresh artistic possibilities for creators in the 3D domain. It constructs these avatars through the use of neural radiance fields, applying state-of-the-art generative methods referred to as diffusion models. The framework employs a tri-plane representation, which efficiently breaks down the neural radiance field of the avatars, enabling explicit modeling through diffusion and the rendering of images using volumetric techniques. Furthermore, the integration of 3D-aware convolution boosts computational efficiency while ensuring the preservation of diffusion modeling integrity in three-dimensional contexts. The entire avatar generation process is organized hierarchically, making use of cascaded diffusion models to support multi-scale modeling, which further sharpens the details involved in creating avatars. This significant innovation not only transforms the realm of digital avatar production but also fosters enhanced collaboration among artists and developers engaged in this evolving field, paving the way for even more innovative projects in the future.

What is DreamFusion?

Recent progress in text-to-image synthesis has been driven by diffusion models trained on vast collections of image-text pairs. To effectively adapt this approach for 3D synthesis, there is a critical need for large datasets of labeled 3D assets and efficient architectures capable of denoising 3D information, both of which are currently insufficient. This research aims to tackle these obstacles by utilizing an established 2D text-to-image diffusion model to facilitate text-to-3D synthesis. We introduce a groundbreaking loss function based on probability density distillation, enabling a 2D diffusion model to guide the optimization of a parametric image generator effectively. By applying this loss within a DeepDream-inspired framework, we enhance a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent, ensuring its 2D renderings from various angles demonstrate reduced loss. As a result, the generated 3D representation can be viewed from multiple viewpoints, illuminated under different lighting conditions, or integrated seamlessly into a variety of 3D environments. This innovative approach not only addresses existing limitations but also paves the way for the broader application of 3D modeling in both creative and commercial sectors, potentially transforming industries reliant on visual content.

Media

Media

Integrations Supported

Figma Weave
Fuser
Haimeta

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

3d-avatar-diffusion.microsoft.com

Company Facts

Organization Name

DreamFusion

Company Website

dreamfusion3d.github.io

Categories and Features

AI Avatar Generators

Not specified

AI Tools

Not specified

Digital Human

Not specified

Categories and Features

AI Tools

Not specified

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