UCTGAN: Diverse Image Inpainting Based on Unsupervised Cross-Space Translation

CVPR 2020 Lei Zhao Qihang Mo Sihuan Lin Zhizhong Wang Zhiwen Zuo Haibo Chen Wei Xing Dongming Lu

Although existing image inpainting approaches have been able to produce visually realistic and semantically correct results, they produce only one result for each masked input. In order to produce multiple and diverse reasonable solutions, we present Unsupervised Cross-space Translation Generative Adversarial Network (called UCTGAN) which mainly consists of three network modules: conditional encoder module, manifold projection module and generation module... (read more)

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