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First-person (egocentric) and third person (exocentric) videos aredrastically different in nature. The relationship between these two views havebeen studied in recent years, however, it has yet to be fully explored. In thiswork, we introduce two datasets (synthetic and natural/real) containingsimultaneously recorded egocentric and exocentric videos. We also explorerelating the two domains (egocentric and exocentric) in two aspects. First, wesynthesize images in the egocentric domain from the exocentric domain using aconditional generative adversarial network (cGAN). We show that with enoughtraining data, our network is capable of hallucinating how the world would looklike from an egocentric perspective, given an exocentric video. Second, weaddress the cross-view retrieval problem across the two views. Given anegocentric query frame (or its momentary optical flow), we retrieve itscorresponding exocentric frame (or optical flow) from a gallery set. We showthat using synthetic data could be beneficial in retrieving real data. We showthat performing domain adaptation from the synthetic domain to the natural/realdomain, is helpful in tasks such as retrieval. We believe that the presenteddatasets and the proposed baselines offer new opportunities for furtherresearch in this direction. The code and dataset are publicly available.

From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval