Generation of stereo images based on a view synthesis network

Yuan Mau Lo, Chin Chen Chang*, Der Lor Way, Zen Chung Shih

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The conventional warping method only considers translations of pixels to generate stereo images. In this paper, we propose a model that can generate stereo images from a single image, considering both translation as well as rotation of objects in the image. We modified the appearance flow network to make it more general and suitable for our model. We also used a reference image to improve the inpainting method. The quality of images resulting from our model is better than that of images generated using conventional warping. Our model also better retained the structure of objects in the input image. In addition, our model does not limit the size of the input image. Most importantly, because our model considers the rotation of objects, the resulting images appear more stereoscopic when viewed with a device.

Original languageEnglish
Article number3101
JournalApplied Sciences (Switzerland)
Issue number9
StatePublished - 1 May 2020


  • Depth estimation
  • Neural network
  • Semantic segmentation
  • Stereo images
  • View synthesis


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