Modelling and unsupervised learning of symmetric deformable object categories

James Thewlis, Hakan Bilen, Andrea Vedaldi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input. It is well known that objects that have a symmetric structure do not usually result in symmetric images due to articulation and perspective effects. This is often tackled by seeking the intrinsic symmetries of the underlying 3D shape, which is very difficult to do when the latter cannot be recovered reliably from data. We show that, if only raw images are given, it is possible to look instead for symmetries in the space of object deformations. We can then learn symmetries from an unstructured collection of images of the object as an extension of the recently introduced object frame representation, modified so that object symmetries reduce to the obvious symmetry groups in the normalized space. We also show that our formulation provides an explanation of the ambiguities that arise in recovering the pose of symmetric objects from their shape or images and we provide a way of discounting such ambiguities in learning.
Original languageEnglish
Title of host publication32nd Conference on Neural Information Processing Systems (NIPS 2018)
Place of PublicationMontréal, Canada
Number of pages14
Publication statusPublished - 2018
EventThirty-second Conference on Neural Information Processing Systems - Montreal, Canada
Duration: 3 Dec 20188 Dec 2018


ConferenceThirty-second Conference on Neural Information Processing Systems
Abbreviated titleNIPS 2018
Internet address

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