Egocentric Video Understanding

The field studies how systems can understand activities and environments from a person’s moving, first-person viewpoint, where hands, objects, and relevant events may be briefly visible or occluded. Methods combine video-language models and temporal action models with structured scene graphs, long-term memory, 3D reconstruction, and cues such as gaze, audio, and motion sensors. Benchmarks test action recognition and anticipation, procedural step understanding, spatial reasoning, and streaming recall in settings such as kitchens, workplaces, and daily life. Models can describe visible scenes but still struggle to ground answers in precise temporal evidence, maintain consistent spatial memory, and predict intent reliably enough for assistance.