Learning Zero-Shot Material States Segmentation by Implanting Natural Image Patterns in Synthetic Data

The author proposes a method to bridge the gap between real-world complexity and synthetic data precision by implanting patterns from natural images into synthetic scenes, enabling class-agnostic material segmentation. This approach allows for capturing the vast complexity of the real world while maintaining the precision and scale of synthetic data.