Exocentric Video

Exocentric video research focuses on bridging the gap between first-person (egocentric) and third-person (exocentric) perspectives of actions, aiming to generate one view from the other or to learn view-invariant representations of actions. Current research employs generative models, particularly diffusion models, and leverages techniques like multi-view stereo matching, self-supervised learning, and contrastive learning to achieve cross-view translation and action understanding. This work is significant for advancing embodied AI, augmented reality, and human-computer interaction by enabling AI systems to better understand and interact with the world from diverse viewpoints.