The dataset was generated through a mixed (real and synthetic) image data generation method which utilizes real background images and DL-generated human models. It contains 50000 real images depicting urban scenes, populated by synthetic human models in various positions and poses and is suitable for training/evaluating (a) pose estimation, (b) person detection, (c) identity recognition methods. Annotations for 2D bounding boxes of the depicted humans, their IDs and 2D keypoints etc are provided. The 133 3D human models, required by the method, were generated using the Pixel-aligned Implicit Function (PIFu) and full-body images of people from the Clothing Co-Parsing (CCP) dataset. As background images, a subset of the Cityscapes dataset was used. The Cityscapes license prohibits the distribution of any modified versions of itself. Thus, we provide code that can re-generate the exact same dataset, given that the Cityscapes dataset is downloaded by the website of its authors. Code and instructions for re-generating the dataset are provided here. The dataset was developed by Aristotle University of Thessaloniki (AUTH) within the H2020 OpenDR Project.

Due to license restrictions regarding the Cityscapes dataset, we provide code, annotations and 3D human models, which can generate our dataset. The code, the annotations and the 3D human models are licensed under the Apache 2.0 License. https://www.apache.org/licenses/LICENSE-2.0 The final dataset is subjected to the original license of the authors of the Cityscapes dataset. https://www.cityscapes-dataset.com/license/