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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 Point-E?

Recent progress in generating 3D objects from text has shown promising results; nonetheless, many of the leading techniques typically require multiple hours on powerful GPUs to produce just one sample, which stands in stark contrast to the more advanced generative image models that can create samples in a matter of seconds or minutes. In this research, we introduce a novel method for 3D object generation that allows for model creation in merely 1-2 minutes using only a single GPU. Our approach begins with generating a synthetic view through a text-to-image diffusion model, and it is followed by constructing a 3D point cloud using a second diffusion model that is conditioned on the image produced. Although our method has not yet reached the highest quality levels of the best existing techniques, it provides a considerably quicker sampling process, thus serving as a valuable alternative for certain applications. Additionally, we make available our pre-trained point cloud diffusion models, as well as the evaluation code and supplementary models, accessible at this provided URL. This endeavor is intended to encourage further research and innovation in the area of rapid 3D object generation, potentially paving the way for more efficient workflows in the industry.

Media

Media

Integrations Supported

Fuser
Haimeta
Weavy

Integrations Supported

Fuser
Haimeta
Weavy

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

3d-avatar-diffusion.microsoft.com

Company Facts

Organization Name

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com/research/point-e

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