  Synthetic data is becoming increasingly common for training computer vision
models for a variety of tasks. Notably, such data has been applied in tasks
related to humans such as 3D pose estimation where data is either difficult to
create or obtain in realistic settings. Comparatively, there has been less work
into synthetic animal data and it's uses for training models. Consequently, we
introduce a parametric canine model, DynaDog+T, for generating synthetic canine
images and data which we use for a common computer vision task, binary
segmentation, which would otherwise be difficult due to the lack of available
data.
