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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.

What is Deception.ai?

Creating convincingly authentic fake networks that can thwart, confuse, or manipulate adversaries is a complex undertaking that demands considerable time, financial resources, and expertise. Penten’s Deception.ai emerges as a powerful artificial intelligence tool that simplifies the development and execution of these sophisticated fake networks essential for detecting, monitoring, and combating advanced cyber threats. With its intelligent workflow, the platform assists users in constructing their deceptive networks and planning their attack strategies, while also facilitating the deployment of scenarios and the generation of realistic, tailored users alongside engaging content. These constructed users interact with the system, performing tasks that closely resemble genuine human actions, such as sending and receiving emails, editing documents, making phone calls, and conversing with other users. This streamlined methodology allows for the quick creation of a believable environment, which is vital for effectively engaging with adversaries. By harnessing such innovative technology, organizations can significantly bolster their security measures while also gaining tactical advantages in their cyber defense initiatives. Moreover, the ability to seamlessly integrate these fake networks into existing security protocols heightens the overall resilience against cyber threats.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Company Facts

Organization Name

Penten

Date Founded

2014

Company Location

Australia

Company Website

www.penten.com/applied-ai/deception-ai/

Categories and Features

Categories and Features

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