Datasets that allow the training of common objects or human detectors are widely available. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. Likewise, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information as human shapes, are hardly available. To overcome this, usage of synthetic data generation with realistic rendering technologies has recently gained traction and advanced tasks like target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://keeper.mpdl.mpg.de/d/12abb3bb6b12491480d5/.

翻译：支持通用物体或人体检测器训练的数据集已广泛可用。这些数据集以标注的真实图像形式存在，要么需要大量人工投入且极易出现漏标等错误，要么依赖受严格约束的场景（例如VICON系统）。同样，非常规场景（如航拍视角）、野生动物（如野生斑马）或难以获取的信息（如人体姿态）等数据几乎不可得。为克服这一难题，结合真实感渲染技术的合成数据生成方法近年来受到广泛关注，并推动了目标跟踪、人体姿态估计等任务的发展。然而，野生动物等对象通常仍未被此类数据充分表征。本文首先证明，预训练的YOLO检测器无法识别航拍视角下的真实斑马图像。为解决该问题，我们提出一种仅使用合成数据训练动物检测器的方法。首先利用最先进的数据生成框架GRADE创建新型合成斑马数据集，该数据集包含每只斑马的RGB图像、深度图、骨架关节点位置、姿态、体形及实例分割标注。我们在此基础上从头训练YOLO检测器。通过使用两类真实世界数据进行全面评估：（i）互联网上有限的公开数据集；（ii）我们自主采集并手动标注的新数据集，结果表明仅使用合成数据训练即可实现斑马检测。代码、结果、训练模型以及生成的合成数据与真实训练数据均已开源发布在https://keeper.mpdl.mpg.de/d/12abb3bb6b12491480d5/。