Semantic View Synthesis

Hsin-Ping Huang1Hung-Yu Tseng2Hsin-Ying Lee2Jia-Bin Huang3

1UT Austin   2UC Merced   3Virginia Tech


We tackle a new problem of semantic view synthesis --- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input. We build upon recent advances in semantic image synthesis and view synthesis for handling photographic image content generation and view extrapolation. Direct application of existing image/view synthesis methods, however, results in severe ghosting/blurry artifacts. To address the drawbacks, we propose a two-step approach. First, we focus on synthesizing the color and depth of the visible surface of the 3D scene. We then use the synthesized color and depth to impose explicit constraints on the multiple-plane image (MPI) representation prediction process. Our method produces sharp contents at the original view and geometrically consistent renderings across novel viewpoints. The experiments on numerous indoor and outdoor images show favorable results against several strong baselines and validate the effectiveness of our approach.



  author       = "Huang, Hsin-Ping and Tseng, Hung-Yu and Lee, Hsin-Ying and Huang, Jia-Bin",
  title        = "Semantic View Synthesis",
  booktitle    = "European Conference on Computer Vision (ECCV)",
  year         = "2020"
Page source: Po-Han Huang