While there exists a great deal of labeled in-the-wild human data, the same is not true for animals. Manually creating new labels for the full range of animal species would take years of effort from the community. We are also now seeing the emerging potential for computer vision methods in areas like animal conservation, which is an additional motivation for this direction of research. Key to our approach is the ability to easily generate as many labeled training images as we desire across a range of different modalities. To achieve this, we present a new large scale canine motion capture dataset and parametric canine body and texture model. These are used to produce the first large scale, multi-domain, multi-task dataset for canine body analysis comprising of detailed synthetic labels on both real images and fully synthetic images in a range of realistic poses. We also introduce the first pose prior for animals in the form of a variational pose prior for canines which is used to fit the parametric model to images of canines. We demonstrate the effectiveness of our labels for training computer vision models on tasks such as parts-based segmentation and pose estimation and show such models can generalise to other animal species without additional training.

The automatic extraction of animal 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.