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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 GET3D?

We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

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

V2Fun

Date Founded

2025

Company Location

United States

Company Website

v2fun.ai/

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Categories and Features

Categories and Features

AI Tools

Not specified

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