  The automatic extraction of animal \reb{3D} pose from images without markers
is of interest in a range of scientific fields. Most work to date predicts
animal pose from RGB images, based on 2D labelling of joint positions. However,
due to the difficult nature of obtaining training data, no ground truth dataset
of 3D animal motion is available to quantitatively evaluate these approaches.
In addition, a lack of 3D animal pose data also makes it difficult to train 3D
pose-prediction methods in a similar manner to the popular field of body-pose
prediction. In our work, we focus on the problem of 3D canine pose estimation
from RGBD images, recording a diverse range of dog breeds with several
Microsoft Kinect v2s, simultaneously obtaining the 3D ground truth skeleton via
a motion capture system. We generate a dataset of synthetic RGBD images from
this data. A stacked hourglass network is trained to predict 3D joint
locations, which is then constrained using prior models of shape and pose. We
evaluate our model on both synthetic and real RGBD images and compare our
results to previously published work fitting canine models to images. Finally,
despite our training set consisting only of dog data, visual inspection implies
that our network can produce good predictions for images of other quadrupeds --
e.g. horses or cats -- when their pose is similar to that contained in our
training set.
