3D Morphable Models as Spatial Transformer Networks

Bas A. , Huber P., Smith W. A. P. , Awais M., Kittler J.

16th IEEE International Conference on Computer Vision (ICCV), Venice, İtalya, 22 - 29 Ekim 2017, ss.895-903 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iccvw.2017.110
  • Basıldığı Şehir: Venice
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.895-903


In this paper, we show how a 3D Morphable Model (i.e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self-occlusions. The trained localisation part of the network is independently useful since it learns to fit a 3D morphable model to a single image. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images including occlusion, self-occlusion and large pose changes.