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Media Resources
3DiVi Inc., founded in 2011, is one of the leading international developers of AI and machine learning (ML) technologies for computer vision.
The technological basis of the company is the self-developed ML framework 3DiVi Core AI that is used for development of state-of-the-art ML algorithms. 3DiVi Core AI framework consists of three layers: base models, a deep learning platform, and a super-computer cluster. It is available as an AI-as-a-Service to customers interested to produce AI models tailored to their business needs with minimal effort, expertise, and investment. For many clients, compared with building in-house AI infrastructure which may take years and a tremendous amount of investment, using 3DiVi Core AI to develop AI models is much more cost-effective and can substantially reduce the time to market.
This section defines the key elements of 3DiVi’s brand identity, including logo usage, typography, and color palette. A clear and consistent visual style ensures recognition across all communications. It also provides an overview of our products, highlighting their core technologies and screenshots.
Logo
The 3DiVi logo is a core element of our identity. This section provides approved logo variations
Colors
3DiVi’s color palette consists of three core colors. Red is the primary accent color, while black and gray are used for typography and supporting elements.
3DiVi Grey
RGB: 46 46 46 Hex: #4C4C4C
3DiVi Red
RGB: 293 61 51 Hex: #EF3D33
3DiVi Blak
RGB: 35 31 32 Hex: #231F20
Face SDK
Facial and body recognition library for server, mobile and embedded solutions requiring image and streaming video processing
A scalable platform for face, body and situation recognition that processes images and videos, collects the event history and sends real-time notifications on detected events
Integration API and Admin web interface
Identification of masked or partially covered faces
Biometric Anti-Fraud (BAF) is a technology stack for intelligent processing, analysis and recognition of images with an application scenario for remote human identification.
Over 97% Quality of Liveness algorithm according to BPCER
Over 99% Quality of Liveness algorithm according to APCER
Over 99% Quality of the face recognition algorithm