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What is V2Fun?

V2Fun is an all-in-one, web-based platform that leverages artificial intelligence to transform text instructions, reference images, and standard videos into 3D models and animations. This cutting-edge tool allows users to create concept visuals that are not only dimensionally precise but also perspective-accurate, generating high-quality assets from both single and multiple viewpoints through sophisticated text interpretation. With built-in features for prompt enhancement, it enriches user inputs with spatial perspective, physically based rendering (PBR) materials, and intricate texture settings. Furthermore, its image-to-3D and text-to-3D engines preserve the original artistic style, character traits, structural fidelity, and natural illumination of the source materials, enabling rapid iterations across a wide range of applications, including character design, scene construction, game asset development, industrial modeling, and items for 3D printing. The platform also boasts an advanced retopology feature that produces clean, lightweight, editable quad meshes without sacrificing surface detail, while its AI-powered texture generator effectively creates or updates complete sets of PBR materials, ensuring accurate lighting, bump, gloss, and roughness values. Consequently, users are empowered to effortlessly combine various components into their projects, significantly boosting creativity and efficiency throughout the 3D design workflow. This seamless integration of features makes V2Fun an indispensable tool for both novice and experienced designers alike.

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

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

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

V2Fun

Date Founded

2025

Company Location

United States

Company Website

v2fun.ai/

Company Facts

Organization Name

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com/research/point-e

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