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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.
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/