Description
The growing demand for large, diverse datasets in AI and machine learning is often limited by the cost, privacy, and complexity of real-world data collection. Synthetic Data Generation offers a powerful alternative-creating realistic, controllable datasets through computer graphics, generative models, and physics-based simulations.
This approach enables precise control over environments, lighting, and object diversity, while producing data that can match or exceed real-world complexity. In particular, realistic 3D human avatars support applications in computer vision, virtual reality, and ergonomics, providing rich and ethical training data. Beyond human modeling, synthetic data drives progress in robotics, automation, and quality control.
Ultimately, it represents a shift toward scalable, ethical, and customizable data creation-enhancing AI robustness and accelerating innovation across industries.
| Pracovisko fakulty (katedra)/ Department of Faculty | Department of Applied Informatics |
|---|---|
| Tlač postru/ Print poster | Budem požadovať tlač /I hereby required to print the poster in faculty |
