RGBT-Dog: A Parametric Model and Pose Prior for Canine Body Analysis Data Creation

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.