Digital Humans for AR, VR and Metaverse Applications

New augmented reality and virtual reality applications in the Metaverse depend on a detailed representation of how people interact in a wide variety of real-life scenarios.  However, Metaverse growth is severely limited by the complexities and challenges of capturing and annotating real-world 3D human data. Sophisticated machine learning models, supporting the AR and VR apps of the future, are driving the need for synthetic data 

Synthesis AI is capable of supplying a nearly infinite amount of synthetic data for training ML models in AR/VR software development. The synthetic datasets provide pixel-perfect 3D annotations to help build highly robust digital human models for Metaverse applications at a fraction of the cost and time required today.

Digital Human Datasets for Metaverse Applications

Synthesis AI is the technology leader for creating realistic digital humans. Our digital human datasets support the many different types of avatar characteristics, including skin tones, gestures, and facial expressions, needed for next generation virtual innovation in the Metaverse.

We focus on the fine details of human movement and characteristics, as well as human interactions with a given environment, to support the development of augmented and virtual reality applications, avatars and experiences in the Metaverse.

Facial Landmarks Used for Avatar Creation and Emotion Tracking

Creating photorealistic and expressive avatars requires a detailed understanding of facial geometry and movements. Synthesis AI’s high-density 3D facial landmarks enable companies to build models with unprecedented accuracy.

Digital Human Facial Landmarks Examples

  • 100K unique identities spanning sex, age, skin-tone, and ethnicity
  •  Ability to modify a wide range of facial attributes
  •  A proprietary set of 5K 3D facial landmarks
  •  Full control of lighting, environments, and cameras
Complex Pose Estimation

Pose Estimation for Digital Humans

Programmatic control of body landmarks helps build highly efficient and robust pose estimation models. Our pose estimation models are key to recreating lifelike human movements for realistic virtual applications.

Digital Human Pose Estimation Examples

  • Body types
  • Clothing characteristics
  • Arm and hand movements
  • Leg and feet movements
  • Facial movements

Advanced Labeling for Digital Humans

Machine learning models are dependent on accurately labeled data. Synthesis AI’s synthetic data systems are able to provide never-before-available pixel-perfect labels to support advanced model machine learning models required to build metaverse applications.

Dataset Labeling for Digital Humans

  • Detailed Segmentation Maps
  • Depth levels
  • Surface normal
  • 2D/3D landmarks
  • Many more feature labels available
Pixel-perfect set of rich labels
Gesture Recognition

Gesture Recognition for AR/VR Applications

Implement all types of gestures in your virtual animations. We support a wide range of gesture variables to ensure your models are high-performing and realistic. Human gestures are an important attribute within the Metaverse and AR/VR applications for realism and functionality.

Digital Human Gesture Recognition Variables

  • Body types
  • Camera angles
  • Backgrounds
  • Environments
  • And many more gesture configurations available

Activity Classification for Digital Humans

AR, VR, and Metaverse applications need to transport everyday, real-world activities into virtual space. To provide a lifelike experience, the classification modeling for activities is extremely important.

Mapping AR, VR and Metaverse Activities

  • Identities
  • Body types
  • Environments
  • And many more activity mappings

Learn More About Our Synthetic Data Solutions And Schedule A Demo Today

Take a moment and submit a bit more information about the problem you’re trying to solve with synthetic data,  below. We’d love the opportunity to help you build the  AR, VR and Metaverse applications of tomorrow.

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    Synthesis AI speaking at the MetaBeat conference on Oct 4th

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