Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network
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英文摘要
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.
发表机构
- Texas State University（德克萨斯州立大学）
- University of Trento（特伦托大学）
- Cisco（思科）
机构由 AI 辅助整理，请以论文原文为准。
