  First-person (egocentric) and third person (exocentric) videos are
drastically different in nature. The relationship between these two views have
been studied in recent years, however, it has yet to be fully explored. In this
work, we introduce two datasets (synthetic and natural/real) containing
simultaneously recorded egocentric and exocentric videos. We also explore
relating the two domains (egocentric and exocentric) in two aspects. First, we
synthesize images in the egocentric domain from the exocentric domain using a
conditional generative adversarial network (cGAN). We show that with enough
training data, our network is capable of hallucinating how the world would look
like from an egocentric perspective, given an exocentric video. Second, we
address the cross-view retrieval problem across the two views. Given an
egocentric query frame (or its momentary optical flow), we retrieve its
corresponding exocentric frame (or optical flow) from a gallery set. We show
that using synthetic data could be beneficial in retrieving real data. We show
that performing domain adaptation from the synthetic domain to the natural/real
domain, is helpful in tasks such as retrieval. We believe that the presented
datasets and the proposed baselines offer new opportunities for further
research in this direction. The code and dataset are publicly available.
