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What is StatSocial Silhouette?

Silhouetteâ„¢ emerges as a groundbreaking audience intelligence platform that facilitates the identification and engagement of anonymized personas by analyzing earned media habits, influencers, interests, and personalities. Utilizing social data, Silhouetteâ„¢ delivers targeted, actionable insights and media strategies designed specifically for any chosen persona or audience segment. This cutting-edge platform effectively overcomes the challenges of assessing social influencer campaigns by correlating social content interaction with measurable business outcomes and revenue metrics. For the first time, companies can turn their market research, marketing personas, and surveys into large, addressable audience segments. Central to Silhouetteâ„¢ is a detailed social identity graph that incorporates over 1.2 billion profiles from 60 different platforms, alongside more than a million websites, 300 million distinct email accounts, and upwards of one billion mobile devices and IP addresses, solidifying its position as a powerful asset in audience analytics. This vast repository empowers brands to make educated choices and fine-tune their strategies with precision, ultimately enhancing their marketing effectiveness. Such capabilities enable businesses not only to understand their audience better but also to foster deeper connections with them.

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.

Media

Media

Integrations Supported

Microsoft Excel
Pulsar Platform
Winmo

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Not specified

Training Options

Documentation Hub

Company Facts

Organization Name

StatSocial

Date Founded

2012

Company Location

United States

Company Website

www.statsocial.com

Company Facts

Organization Name

NVIDIA

Company Location

United States

Company Website

nv-tlabs.github.io/GET3D/

Categories and Features

Audience Intelligence

Not specified

Brand Intelligence

Not specified

Categories and Features

AI Tools

Not specified

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