EgoHOS - Egocentric Hand-Object Segmentation

Overview

Fine-grained hand-object segmentation system designed for egocentric (first-person) videos. EgoHOS provides pixel-level segmentation masks that precisely separate hands from objects and background, enabling detailed analysis of hand-object interactions. The system outputs colorful mask overlays that make hand regions visually distinct and easy to analyze.

Key advantage: Pixel-level accuracy for understanding hand-object boundaries and contact regions, surpassing bounding box or keypoint approaches for interaction understanding.

When to Use This Skill

This skill should be used when:

• Need pixel-accurate hand and object masks in egocentric videos
• Analyzing hand-object manipulation and interactions
• Studying contact regions between hands and objects
• Creating training data for segmentation models
• Applications requiring precise hand shape and outline
• Research in fine-grained activity recognition
• Building systems that need to understand hand-object contact
• Generating annotated videos with segmentation overlays