DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation
This addresses the problem of limited training data for computer vision tasks involving animals, particularly canines, for researchers and practitioners in synthetic data generation.
The authors tackled the lack of synthetic animal data by introducing DynaDog+T, a parametric canine model for generating synthetic canine images, and demonstrated its use for binary segmentation, achieving a Dice score of 0.87 on a test set.
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.