  Accurately annotated image datasets are essential components for studying
animal behaviors from their poses. Compared to the number of species we know
and may exist, the existing labeled pose datasets cover only a small portion of
them, while building comprehensive large-scale datasets is prohibitively
expensive. Here, we present a very data efficient strategy targeted for pose
estimation in quadrupeds that requires only a small amount of real images from
the target animal. It is confirmed that fine-tuning a backbone network with
pretrained weights on generic image datasets such as ImageNet can mitigate the
high demand for target animal pose data and shorten the training time by
learning the the prior knowledge of object segmentation and keypoint estimation
in advance. However, when faced with serious data scarcity (i.e., $<10^2$ real
images), the model performance stays unsatisfactory, particularly for limbs
with considerable flexibility and several comparable parts. We therefore
introduce a prior-aware synthetic animal data generation pipeline called PASyn
to augment the animal pose data essential for robust pose estimation. PASyn
generates a probabilistically-valid synthetic pose dataset, SynAP, through
training a variational generative model on several animated 3D animal models.
In addition, a style transfer strategy is utilized to blend the synthetic
animal image into the real backgrounds. We evaluate the improvement made by our
approach with three popular backbone networks and test their pose estimation
accuracy on publicly available animal pose images as well as collected from
real animals in a zoo.
