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Description

The Citrine Platform integrates state-of-the-art AI technologies with advanced data management systems, offering user-friendly interfaces and robust security measures that comply with industry standards, all while being securely hosted in the cloud. It effectively captures, organizes, and retains comprehensive information regarding the development of materials and chemicals, spanning from procurement to processing and characterization. By minimizing unnecessary experiments, users can swiftly access pertinent data sets. With its powerful AI features, the Citrine Platform accelerates the identification of high-performing materials. Its predictive models analyze materials' performance based on processing, composition, and synthesis details, guiding users on the next experiments to undertake in order to meet their objectives. Furthermore, the Citrine Platform ensures the integrity and confidentiality of your data, domain expertise, and models through stringent protective measures. The platform is backed by ISO27001 certification and comprehensive documentation, providing additional assurance of its commitment to security and best practices. This attention to detail and dedication to user needs makes the Citrine Platform a valuable tool for the materials science community.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

No details available.

Integrations

No details available.

Pricing Details

No price information available.
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

Citrine Informatics

Founded

2013

Country

United States

Website

citrine.io/product/what-is-the-citrine-platform/

Vendor Details

Company Name

DreamFusion

Website

dreamfusion3d.github.io

Product Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Product Features

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