SeGAN: Segmenting and Generating the Invisible

@article{Ehsani2017SeGANSA,
  title={SeGAN: Segmenting and Generating the Invisible},
  author={Kiana Ehsani and Roozbeh Mottaghi and Ali Farhadi},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2017},
  pages={6144-6153}
}
Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects. Doing so requires knowing which pixels to paint (segmenting the invisible parts of objects) and what color to paint them (generating the invisible parts). Our proposed novel solution, SeGAN, jointly… CONTINUE READING

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