  Nowadays, there is a wide availability of datasets that enable the training
of common object detectors or human detectors. 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. On the other hand, uncommon
scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain
information, such as human shapes, are hardly available. To overcome this,
synthetic data generation with realistic rendering technologies has recently
gained traction and advanced research areas such as 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://eliabntt.github.io/grade-rr.
