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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

SpAItial is an innovative AI platform dedicated to the creation and implementation of Spatial Foundation Models (SFMs), a groundbreaking category of generative AI systems that excel in generating and interpreting 3D environments while maintaining physical realism and spatial intelligence. Unlike conventional models that independently generate images or text, SpAItial's advanced technology works directly with 3D structures from the beginning, effectively capturing aspects such as geometry, materials, lighting, and physics to create immersive and interactive worlds. Its premier model, Echo-2, possesses the remarkable ability to convert a single image into a fully navigable, photorealistic 3D scene using cutting-edge techniques like Gaussian splatting, which allows users to explore and render environments in real time. This platform is designed with a robust, physically grounded comprehension of space-time, enabling the AI to analyze how objects are situated, interact, and develop within a given environment, eschewing the disjointed outputs typical of traditional generative AI. This innovative methodology not only mitigates the inconsistencies often found in standard generative AI systems but also facilitates a more precise and realistic simulation of environments, paving the way for exciting new applications in virtual reality and beyond.

API Access

Has API

API Access

Has API

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Integrations

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Pricing Details

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Free Trial
Free Version

Pricing Details

Free
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

spAItial

Country

United States

Website

app.spaitial.ai/

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Product Features

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