  We introduce a new benchmark analysis focusing on 3D canine pose estimation
from monocular in-the-wild images. A multi-modal dataset 3DDogs-Lab was
captured indoors, featuring various dog breeds trotting on a walkway. It
includes data from optical marker-based mocap systems, RGBD cameras, IMUs, and
a pressure mat. While providing high-quality motion data, the presence of
optical markers and limited background diversity make the captured video less
representative of real-world conditions. To address this, we created
3DDogs-Wild, a naturalised version of the dataset where the optical markers are
in-painted and the subjects are placed in diverse environments, enhancing its
utility for training RGB image-based pose detectors. We show that using the
3DDogs-Wild to train the models leads to improved performance when evaluating
on in-the-wild data. Additionally, we provide a thorough analysis using various
pose estimation models, revealing their respective strengths and weaknesses. We
believe that our findings, coupled with the datasets provided, offer valuable
insights for advancing 3D animal pose estimation.
