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Description
Recent advancements in the realm of text-to-image synthesis have emerged from diffusion models that have been trained on vast amounts of image-text pairs. To successfully transition this methodology to 3D synthesis, it would necessitate extensive datasets of labeled 3D assets alongside effective architectures for denoising 3D information, both of which are currently lacking. In this study, we address these challenges by leveraging a pre-existing 2D text-to-image diffusion model to achieve text-to-3D synthesis. We propose a novel loss function grounded in probability density distillation that allows a 2D diffusion model to serve as a guiding principle for the optimization of a parametric image generator. By implementing this loss in a DeepDream-inspired approach, we refine a randomly initialized 3D model, specifically a Neural Radiance Field (NeRF), through gradient descent to ensure its 2D renderings from various angles exhibit a minimized loss. Consequently, the 3D representation generated from the specified text can be observed from multiple perspectives, illuminated with various lighting conditions, or seamlessly integrated into diverse 3D settings. This innovative method opens new avenues for the application of 3D modeling in creative and commercial fields.
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
API Access
Has API
Integrations
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Integrations
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Pricing Details
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Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DreamFusion
Website
dreamfusion3d.github.io
Vendor Details
Company Name
V2Fun
Founded
2025
Country
United States
Website
v2fun.ai/