Abstract
Accurate 3D human pose estimation from single images
is possible with sophisticated deep-net architectures that
have been trained on very large datasets. However, this still
leaves open the problem of capturing motions for which no
such database exists. Manual annotation is tedious, slow,
and error-prone. In this paper, we propose to replace most
of the annotations by the use of multiple views, at training
time only. Specifically, we train the system to predict the
same pose in all views. Such a consistency constraint is necessary but not sufficient to predict accurate poses. We therefore complement it with a supervised loss aiming to predict
the correct pose in a small set of labeled images, and with a
regularization term that penalizes drift from initial predictions. Furthermore, we propose a method to estimate camera pose jointly with human pose, which lets us utilize multiview footage where calibration is difficult, e.g., for pan-tilt
or moving handheld cameras. We demonstrate the effectiveness of our approach on established benchmarks, as well as
on a new Ski dataset with rotating cameras and expert ski
motion, for which annotations are truly hard to obtain