2025 • AI / Machine Learning

Synthetic Data Pipelines

Explore Project CCSC Poster

0 %

Training Efficiency

Improvement in model training

0 K+

Synthetic Images

Generated for training datasets

Body Keypoints

Tracked per human pose

Real-time

Inference

30+ FPS pose estimation

Project Overview

Developed advanced synthetic data generation pipelines using TensorFlow and HRNet for computer vision applications, enabling a 40% improvement in model training efficiency for pose estimation tasks.

This research project focused on addressing the challenge of limited training data in human pose estimation. By generating photorealistic synthetic images with accurate keypoint annotations, we significantly reduced the need for expensive manual labeling while improving model generalization.

The pipeline integrates multiple rendering engines, domain randomization techniques, and automated annotation systems to produce diverse training datasets that closely mirror real-world conditions.

My Role

Pipeline Architecture

Designed end-to-end data generation pipeline with modular components for rendering, augmentation, and annotation.

Model Training

Implemented HRNet-based pose estimation models with custom loss functions optimized for synthetic data.

Domain Randomization

Developed randomization strategies for lighting, textures, and backgrounds to improve model robustness.

Performance Analysis

Conducted extensive benchmarking and ablation studies to validate pipeline effectiveness.

Technical Stack

Deep Learning

TensorFlow 2.x Primary framework

PyTorch Model prototyping

HRNet Pose estimation

CUDA/cuDNN GPU acceleration

Computer Vision

OpenCV Image processing

Blender 3D rendering

Albumentations Data augmentation

COCO Format Annotation standard

Infrastructure

Python Core language

NumPy/Pandas Data processing

Weights & Biases Experiment tracking

Docker Containerization

Challenges & Solutions

Domain Gap

Challenge: Models trained purely on synthetic data showed significant performance degradation when tested on real images due to the domain gap between rendered and captured imagery.

Solution: Implemented extensive domain randomization including varied lighting conditions, diverse backgrounds, and photorealistic material shaders. Added noise injection and camera simulation to bridge the reality gap.

Rendering Speed

Challenge: High-fidelity rendering was extremely slow, taking minutes per frame, making large-scale dataset generation impractical.

Solution: Developed a hybrid rendering approach combining fast rasterization for base geometry with selective ray-tracing for critical features. Parallelized across multiple GPUs for 50x speedup.

Pose Diversity

Challenge: Generated poses lacked the natural variation and biomechanical constraints found in real human movement, leading to unrealistic training data.

Solution: Integrated motion capture databases and physics-based animation systems. Applied biomechanical constraints to ensure anatomically valid poses while maintaining diversity.

Key Learnings

"This project demonstrated how thoughtful synthetic data generation can democratize access to high-quality training data, reducing the barrier to entry for computer vision research."

Deep understanding of domain adaptation and transfer learning techniques

Practical experience with 3D rendering pipelines and photorealistic image synthesis

Skills in large-scale data processing and distributed computing

Research methodology including ablation studies and statistical validation

Explore More

CCSC Poster

Conference Presentation

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Blender

3D Rendering engine

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TensorFlow

Framework documentation

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