Reviews: Modelling and unsupervised learning of symmetric deformable object categories

Summary: This work propose an approach to model symmetries in deformable object categories in an unsupervised manner. This approach has been demonstrated to work for objects with bilateral symmetry (identifying symmetries in human faces using CelebA dataset, cats using cats head dataset, on cars with a synthetic car dataset), and finally for rotational symmetry on a protein structure. Pros: Overview of the problem and associated challenges. The proposed approach seems a natural way to establish dense correspondences for non-rigid objects given two views of same object category (say example in Figure-3). In my opinion, correspondences for non-rigid/deformable objects is far more important problem than symmetry (with a potential impact on numerous problems including non rigid 3D reconstruction, wide baseline disparity estimation, human analysis etc).