Abstract
The labeled data required to learn pose estimation for
articulated objects is difficult to provide in the desired quantity, realism, density, and accuracy. To address this issue,
we develop a method to learn representations, which are
very specific for articulated poses, without the need for labeled training data. We exploit the observation that the object pose of a known object is predictive for the appearance in any known view. That is, given only the pose and
shape parameters of a hand, the hand’s appearance from
any viewpoint can be approximated. To exploit this observation, we train a model that – given input from one view
– estimates a latent representation, which is trained to be
predictive for the appearance of the object when captured
from another viewpoint. Thus, the only necessary supervision is the second view. The training process of this model
reveals an implicit pose representation in the latent space.
Importantly, at test time the pose representation can be inferred using only a single view. In qualitative and quantitative experiments we show that the learned representations capture detailed pose information. Moreover, when
training the proposed method jointly with labeled and unlabeled data, it consistently surpasses the performance of its
fully supervised counterpart, while reducing the amount of
needed labeled samples by at least one order of magnitude.