Developing driving simulations that look more life-like
Today’s driving simulators have a big problem: They don’t look realistic enough, particularly background objects, such as trees, and road markings. But researchers have developed a new way to create photorealistic images for simulators, paving the way for better testing of driverless cars.
We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable…
In recent years, graph neural network (GNN) based models showed promising results in simulating complex physical systems. However, training dedicated graph network simulator can be costly, as most models are confined to fully supervised training. Extensive data generated from traditional simulators is required to train the model. It remained unexplored…
Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they’ve proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering…