  Our goal is to recover the 3D shape and pose of dogs from a single image.
This is a challenging task because dogs exhibit a wide range of shapes and
appearances, and are highly articulated. Recent work has proposed to directly
regress the SMAL animal model, with additional limb scale parameters, from
images. Our method, called BARC (Breed-Augmented Regression using
Classification), goes beyond prior work in several important ways. First, we
modify the SMAL shape space to be more appropriate for representing dog shape.
But, even with a better shape model, the problem of regressing dog shape from
an image is still challenging because we lack paired images with 3D ground
truth. To compensate for the lack of paired data, we formulate novel losses
that exploit information about dog breeds. In particular, we exploit the fact
that dogs of the same breed have similar body shapes. We formulate a novel
breed similarity loss consisting of two parts: One term encourages the shape of
dogs from the same breed to be more similar than dogs of different breeds. The
second one, a breed classification loss, helps to produce recognizable
breed-specific shapes. Through ablation studies, we find that our breed losses
significantly improve shape accuracy over a baseline without them. We also
compare BARC qualitatively to WLDO with a perceptual study and find that our
approach produces dogs that are significantly more realistic. This work shows
that a-priori information about genetic similarity can help to compensate for
the lack of 3D training data. This concept may be applicable to other animal
species or groups of species. Our code is publicly available for research
purposes at https://barc.is.tue.mpg.de/.
