Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

Virtualized traffic via various simulation models and real‐world traffic data are promising
approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the …

We present a system for training deep neural networks for object detection using synthetic
images. To handle the variability in real-world data, the system relies upon the technique of …

Using synthetic data for training deep neural networks for robotic manipulation holds the
promise of an almost unlimited amount of pre-labeled training data, generated safely out of …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural
world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from …

Multi-task convolutional neural networks (CNNs) have shown impressive results for certain
combinations of tasks, such as single-image depth estimation (SIDE) and semantic …

End-to-end approaches to autonomous driving have high sample complexity and are difficult
to scale to realistic urban driving. Simulation can help end-to-end driving systems by …

We present structured domain randomization (SDR), a variant of domain randomization
(DR) that takes into account the structure of the scene in order to add context to the …

We introduce Synscapes--a synthetic dataset for street scene parsing created using
photorealistic rendering techniques, and show state-of-the-art results for training and …