Synthetic Data Generation for AI Training
Automated pipelines using Blender and UE5 for generating training data.
Overview
Created comprehensive synthetic data generation pipelines that produce photorealistic training data for various computer vision AI tasks. System employs domain randomization and physically-based rendering to maximize sim-to-real transfer.
Challenge
Acquiring large-scale labeled real-world data is expensive and time-consuming. Synthetic data must be diverse and realistic enough to train AI models that generalize to real scenarios.
Solution
Built automated pipelines in Blender and Unreal Engine 5 with extensive domain randomization. Implemented PBR materials, realistic lighting, and physics simulation. Developed tools for automatic annotation and quality control.
Impact
Generated millions of labeled training samples, reducing data acquisition costs and legal challenges. AI models trained on synthetic data achieve performance comparable to real-data trained models.
Key Highlights
- Employed domain randomization and PBR materials
- Closed the sim-to-real gap for AI training
- Fully automated pipeline with quality control