- ✅ 25Q2 - 0.38.2 - Unreal Engine 5 MetaHuman native support 🚀
- ✅ 25Q2 - 0.38.2 - Redesigned Nuitrack App
- ✅ 24Q4 - 0.37.24 - Unreal Engine 5 is now available directly from official Fab marketplace
- ✅ 24Q2 - 0.37.24 - Write CSV files using Nuitrack App. No knowledge of C++ is required!
- TouchDesigner - the official release is now available.
- 🎥 Multisensor tracking Please refer to this page for details on how to participate in Nuitrack Holistic beta testing program.
- ⚙️ Failure cases recorder
Coming soon:
- 🔜 UE Niagara particle system support
- 🔜 Body measurements API
- 🔜 Holistic skeletal tracking with multiple sensors
- 🔜 Major update on tracking accuracy
Nuitrack™ is the 3D body / skeletal tracking library for 3D / depth sensors developed by 3DiVi Inc.
It enables motion-based applications for any widespread:
- 3D sensors - Orbbec Astra2 / Persee2 / Femto Bolt / Femto Mega, Kinect v1/v2, Kinect Azure, Intel Realsense, Asus Xtion, LIPS, Structure Sensor, etc.
- hardware platforms - x64, x86, ARMv7, ARMv8
- OSes - Windows, Linux, Android, iOS
Inspired by first Microsoft Kinect, Nuitrack™'s mission is to provide strong skeletal tracking baseline for the next generation of immersive and analytical applications.
Think of it as a "Kinect for everything".
With its performance and flexibility Nuitrack™ is capable to support the wide range of applications:
- from a real-time gesture recognition on Raspberry Pi4
- to a large-scale multisensor analytical systems
After being launched with any supported depth sensor Nuitrack provides:
| Depth map / 3D Point Cloud | Scene analysis, key elements like floor plane | Persons tracking and pixel-perfect segmentation for each of them |
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| Highly-sophisticated 3D Skeletal Tracking for each person | Basic Facial Analysis like age and emotion detection | Hand Tracker and Gesture Recognition high-level APIs for UI control |
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Nuitrack has two Skeletal Tracking engines:
- "classical" - fast, stable and lightweight, highly-optimized for embedded hardware and limited CPU usage
- AI - deep-learning based engine, which provides greater coverage for complex poses
Essentially Nuitrack provides a human-centric spatial understanding tool for your applications to engage with a user in a natural and intelligent way.
It's as quick and simple as 1-2-3:
- Download the Nuitrack Runtime package for your Platform of choice
- Install it, in case of any issues please follow the Installation Instructions
- Just plug-in your sensor and launch Nuitrack executable from start menu
| C++ | C# | Python |
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| Unity | Unreal Engine 5 Blueprints | 🆕 Touch Designer | 🆕 Docker |
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- Nuitrack provides the ability to embed it into your application, which means it does not need to be separately installed on the PC of your customers, for more information, see here
| Resource | Description |
|---|---|
| Nuitrack.com | general information and license purchasing |
| Licensing Dashboard | licenses/subscriptions management |
| Troubleshooting page | known issues with resolution |
| Community Forum | troubleshooting, feature discussions |
| Documentation | documentation index |
| Runtime Components | packages for all supported platforms |
| C++/C# API | auto-generated API reference (Doxygen) |
| C++/C# Examples | These basic examples demonstrate how to use Nuitrack SDK |
| iOS [beta] | Get started with Nuitrack for iOS |
- Games and Training (Fitness, Dance Lessons)
- Medical Rehabilitation
- Smart Home
- Natural/Gesture-based User Interface (NUI)
- Full Body Tracking for AR / VR
- Audience Analytics
- Robot Vision
Nuitrack is widely used in serious research, here are just a few selected references:
- Detecting Short Distance Throwing Motions Using RGB-D Camera
- Planning Collision-Free Robot Motions in a Human–Robot Shared Workspace via Mixed Reality and Sensor-Fusion Skeleton Tracking
- Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network
- Towards a Live Feedback Training System: Interchangeability of Orbbec Persee and Microsoft Kinect for Exercise Monitoring
- ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments
- Robot-Assisted Gait Self-Training: Assessing the Level Achieved
