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arXiv · 2002.03219

Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network

Gaowen Liu·Hao Tang·Hugo Latapie·Yan Yan
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Abstract

Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a challenging task since egocentric view sometimes is remarkably different from exocentric view. Thus, transforming the appearances across the two views is a non-trivial task. To this end, we propose a novel Parallel Generative Adversarial Network (P-GAN) with a novel cross-cycle loss to learn the shared information for generating egocentric images from exocentric view. We also incorporate a novel contextual feature loss in the learning procedure to capture the contextual information in images. Extensive experiments on the Exo-Ego datasets show that our model outperforms the state-of-the-art approaches.

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BibTeXRIS

Gaowen Liu, Hao Tang, Hugo Latapie, Yan Yan. 2020-02-08. Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network. https://arxiv.org/abs/2002.03219

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