SeGAN: Segmenting and Generating the Invisible

  title={SeGAN: Segmenting and Generating the Invisible},
  author={Kiana Ehsani and Roozbeh Mottaghi and Ali Farhadi},
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
Recent Discussions
This paper has been referenced on Twitter 129 times over the past 90 days. VIEW TWEETS


Publications referenced by this paper.
Showing 1-10 of 59 references

Similar Papers

Loading similar papers…