Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

arXiv preprint

Shangzhe Wu

Oxford VGG

Christian Rupprecht

Oxford VGG

Andrea Vedaldi

Oxford VGG


Abstract

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.


Results

Reconstructing human faces


Reconstructing face paintings

(model trained on CelebA real faces without fine-tuning)


Reconstructing abstract faces

(model trained on CelebA real faces without fine-tuning)


Reconstructing cat Faces


Reconstructing cars


Re-lighting effects


Visualizing symmetry planes

Symmetry plane

Visualizing asymmetry

Visualizing asymmetry

Results on video frames from VoxCeleb

(applying model trained on CelebA faces frame-by-frame without fine-tuning)



Citation

@misc{Wu_2019,
  author={Shangzhe Wu and Christian Rupprecht and Andrea Vedaldi},
  title={Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild},
  year={2019},
  eprint={1911.11130},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}