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.
翻译:合成数据在培训各种任务的计算机视觉模型方面越来越普遍,值得注意的是,这类数据被用于与人类有关的任务,例如3D构成估计,在现实环境中,数据或难以创造或获取。相比之下,合成动物数据方面的工作较少,用于培训模型。因此,我们引入了合成犬模型DynaDog+T,用于生成合成犬图像和数据,我们用于共同的计算机视觉任务,即二元分解,否则由于缺乏现有数据,很难做到这一点。