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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.

What is Magic3D?

By integrating image conditioning techniques with a prompt-based editing strategy, we provide users with groundbreaking methods for manipulating 3D synthesis, thus opening doors to a plethora of creative opportunities. Magic3D stands out for its ability to generate highly detailed 3D textured mesh models derived from textual prompts. It utilizes a coarse-to-fine methodology that combines both low- and high-resolution diffusion priors, which effectively captures the 3D representation of the intended subject. Additionally, Magic3D generates 3D content with supervision that is eight times higher in resolution than that of DreamFusion, all while operating at double the speed. After creating an initial rough model from the provided text prompt, we can modify aspects of the prompt and fine-tune both the NeRF and 3D mesh models, ultimately leading to an improved high-resolution 3D mesh. This flexibility not only fosters greater creativity among users but also optimizes the workflow for crafting intricate 3D visualizations, ensuring a more efficient creative process. The seamless integration of these technologies empowers creators to push the boundaries of their artistic expressions.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

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

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com/research/point-e

Company Facts

Organization Name

Magic3D

Company Website

research.nvidia.com/labs/dir/magic3d/

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