Sky is not the limit: Semantic-aware sky replacement

Yi Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Ming Hsuan Yang

Research output: Contribution to journalConference articlepeer-review

53 Citations (Scopus)


Skies are common backgrounds in photos but are often less interesting due to the time of photographing. Professional photographers correct this by using sophisticated tools with painstaking efforts that are beyond the command of ordinary users. In this work, we propose an automatic background replacement algorithm that can generate realistic, artifact-free images with a diverse styles of skies. The key idea of our algorithm is to utilize visual semantics to guide the entire process including sky segmentation, search and replacement. First we train a deep convolutional neural network for semantic scene parsing, which is used as visual prior to segment sky regions in a coarse-to-fine manner. Second, in order to find proper skies for replacement, we propose a data-driven sky search scheme based on semantic layout of the input image. Finally, to re-compose the stylized sky with the original foreground naturally, an appearance transfer method is developed to match statistics locally and semantically. We show that the proposed algorithm can automatically generate a set of visually pleasing results. In addition, we demonstrate the effectiveness of the proposed algorithm with extensive user studies.

Original languageEnglish
Article numbera149
JournalACM Transactions on Graphics
Issue number4
Publication statusPublished - 2016 Jul 11
EventACM SIGGRAPH 2016 - Anaheim, United States
Duration: 2016 Jul 242016 Jul 28

Bibliographical note

Publisher Copyright:
© 2016 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design


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