Thesis Description & Objectives
Digital human models are essential for applications like training and monitoring systems, where animation realism is crucial. NVIDIA Omniverse offers a powerful platform for testing these animations. This project aims to evaluate and enhance the realism of digital human animations for human monitoring micro-simulation applications.
This thesis will focus on testing digital human animations in NVIDIA Omniverse for various environments. The candidate will integrate human operators using motion capture techniques with wearable suits and log synthetic sensor data, including biometric data. The project will also involve synthetic data annotation and develop a testing framework layer that integrates with an in-house Test Automation Framework (TAF). Additionally, the project will benchmark performance against Unreal Engine and Unity while incorporating animation
generative models.
Objectives:
1. Animation Realism Evaluation: Assess and enhance digital human animations for human monitoring scenarios.
2. Integration of Human Operators: Incorporate human operators into various simulated environments.
3. Synthetic Data Logging: Log synthetic sensor data, including biometric data, during simulations.
4. Synthetic Data Annotation: Develop methodologies for annotating synthetic data for machine learning model training.
5. Motion Capture Integration: Use wearable motion capture suits to improve animation fidelity.
6. Animation Generative Models: Integrate Generative AI to enhance animation quality and variability.
7. Benchmarking Across Platforms: Compare digital human animations in Omniverse with those in Unreal Engine and Unity.
8. Testing Framework Development: Develop a testing framework layer that integrates with the existing TAF for automated scenario testing.
