

Egolife
Overview :
EgoLife is an AI assistant project focused on long-term, multi-modal, multi-view daily life. The project generated approximately 50 hours of video data by recording the shared living experiences of six volunteers for a week, covering daily activities and social interactions. Its multi-modal data (including video, gaze, and IMU data) and multi-view camera system provide rich contextual information for AI research. Furthermore, the project introduces the EgoRAG framework for addressing long-term context understanding tasks, advancing AI's capabilities in complex environments.
Target Users :
EgoLife is suitable for AI researchers and developers, particularly teams focusing on long-term context understanding, multi-modal fusion, multi-view video analysis, and social interaction research. Its rich dataset and framework provide strong support for research in these areas.
Use Cases
Researchers can use the EgoLife dataset to train AI models to understand event development in long-term videos.
Developers can build new video analysis tools for smart homes or health monitoring based on EgoLife's multi-modal data.
Educators can utilize the social interaction data in the EgoLife project to study human behavioral patterns.
Features
Long-term event connection analysis: Supports long-term event analysis across hours and days through continuous week-long recording.
Synchronized multi-modal data: Integrates video, gaze, and IMU data, providing rich sensory information.
Multi-view video recording: Provides comprehensive environmental perspectives using 15 GoPro cameras and first-person perspective glasses.
3D scan support: Provides 3D scan data of the house and participants, supporting 3D application development.
Rich annotated data: Includes transcriptions, dense captions, etc., supporting model training.
EgoLifeQA benchmark: Provides a benchmark dataset for long-term context tasks.
EgoRAG framework: A technical framework for addressing long-term context understanding tasks.
Supports multi-modal AI model training: Such as the EgoGPT model, promoting the development of multi-modal AI.
How to Use
1. Visit the EgoLife project website to learn about the project background and dataset details.
2. Download the relevant datasets, including video, 3D scans, and annotated data.
3. Use the EgoLifeQA benchmark to evaluate model performance on long-term context tasks.
4. Utilize the EgoRAG framework to develop or optimize AI models for handling complex multi-modal data.
5. Leverage multi-view data from GoPro cameras and first-person perspective glasses to develop new applications or research.
6. Apply the trained model to real-world scenarios such as smart homes, health monitoring, or social analysis.
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