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Synthetic data is becoming increasingly common for training computer visionmodels for a variety of tasks. Notably, such data has been applied in tasksrelated to humans such as 3D pose estimation where data is either difficult tocreate or obtain in realistic settings. Comparatively, there has been less workinto synthetic animal data and it's uses for training models. Consequently, weintroduce a parametric canine model, DynaDog+T, for generating synthetic canineimages and data which we use for a common computer vision task, binarysegmentation, which would otherwise be difficult due to the lack of availabledata.

DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation