  We present the first method to perform automatic 3D pose, shape and texture
capture of animals from images acquired in-the-wild. In particular, we focus on
the problem of capturing 3D information about Grevy's zebras from a collection
of images. The Grevy's zebra is one of the most endangered species in Africa,
with only a few thousand individuals left. Capturing the shape and pose of
these animals can provide biologists and conservationists with information
about animal health and behavior. In contrast to research on human pose, shape
and texture estimation, training data for endangered species is limited, the
animals are in complex natural scenes with occlusion, they are naturally
camouflaged, travel in herds, and look similar to each other. To overcome these
challenges, we integrate the recent SMAL animal model into a network-based
regression pipeline, which we train end-to-end on synthetically generated
images with pose, shape, and background variation. Going beyond
state-of-the-art methods for human shape and pose estimation, our method learns
a shape space for zebras during training. Learning such a shape space from
images using only a photometric loss is novel, and the approach can be used to
learn shape in other settings with limited 3D supervision. Moreover, we couple
3D pose and shape prediction with the task of texture synthesis, obtaining a
full texture map of the animal from a single image. We show that the predicted
texture map allows a novel per-instance unsupervised optimization over the
network features. This method, SMALST (SMAL with learned Shape and Texture)
goes beyond previous work, which assumed manual keypoints and/or segmentation,
to regress directly from pixels to 3D animal shape, pose and texture. Code and
data are available at https://github.com/silviazuffi/smalst.
