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Description
We create a three-dimensional signed distance field (SDF) and a textured field using two latent codes. DMTet is employed to derive a 3D surface mesh from the SDF, and we sample the texture field at the surface points to obtain color information. Our training incorporates adversarial losses focused on 2D images, specifically utilizing a rasterization-based differentiable renderer to produce both RGB images and silhouettes. To distinguish between genuine and generated inputs, we implement two separate 2D discriminators—one for RGB images and another for silhouettes. The entire framework is designed to be trainable in an end-to-end manner. As various sectors increasingly transition towards the development of expansive 3D virtual environments, the demand for scalable tools that can generate substantial quantities of high-quality and diverse 3D content has become apparent. Our research endeavors to create effective 3D generative models capable of producing textured meshes that can be seamlessly integrated into 3D rendering engines, thereby facilitating their immediate application in various downstream uses. This approach not only addresses the scalability challenge but also enhances the potential for innovative applications in virtual reality and gaming.
Description
V2Fun is a comprehensive, browser-based platform that utilizes AI to facilitate the creation of 3D models and animations from text prompts, reference images, and standard videos. This innovative tool enables users to produce concept images that are not only structurally accurate but also perspective-correct, while generating high-quality assets from both single and multi-view images through advanced text processing. With features like built-in prompt optimization, it enhances inputs with spatial perspective, physically based rendering (PBR) materials, and detailed texture parameters. Additionally, its image-to-3D and text-to-3D engines maintain the artistic style, character characteristics, structural integrity, and natural lighting of the original inputs, allowing for quick iterations across various applications such as character creation, scene design, game assets, industrial models, and objects suitable for 3D printing. The platform's intelligent retopology feature delivers clean, lightweight, and editable quad meshes without compromising surface intricacy, while its AI-driven texture generator efficiently creates or replaces entire sets of PBR materials that include precise lighting, bump, gloss, and roughness data. As a result, users can seamlessly integrate various elements into their projects, enhancing creativity and productivity in the 3D design process.
API Access
Has API
No
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
NVIDIA
Country
United States
Website
nv-tlabs.github.io/GET3D/
Vendor Details
Company Name
V2Fun
Founded
2025
Country
United States
Website
v2fun.ai/