dataset, synthetic data, unreal engine, Compositional Learning

This contribution presents a framework for a generation of synthetic images. The framework is built on top of the Unreal Engine 4, a software kit capable of rendering realistic images. Besides image data, additional label information, such as depth, normal maps and object components masks, are generated. Hierarchical nature of generated labels corresponds to hierarchical representations which we want to be captured by the neural network. Such labels enable training of deep models in a compositional manner. This leads to the better understanding of the internal representations of the models and acceleration of the learning procedure. The framework allows users to render arbitrary scenes and objects according to their specific domain.