Advancing Hand-Object Tracking with Synthetic Data

This research explores the use of synthetic data to improve hand-object tracking, a critical area for robotics and human-computer interaction. The use of synthetic data could significantly reduce the need for real-world data collection, accelerating development and enabling broader applications.

• •Leverages synthetic data to train hand-object tracking models.
• •Potentially reduces reliance on costly real-world data.
• •Aims to improve generalizability across different scenarios.