Hugging Face Trending Papers
HelloWorld: Towards Practical Applications of Generative Driving World Models
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arXiv Computer Vision
Sep 25
HelloWorld: Towards Practical Applications of Generative Driving World Models
HelloWorld is a 2B-parameter driving world model that learns from diverse video data to generate controllable driving scenarios. It uses ego pose, HD maps, and 3D boxes to produce coherent multi‑sensor outputs, including synchronized seven‑camera RGB and conditional LiDAR. The system is designed for efficient repeated inference and is evaluated on visual quality, control fidelity, cross‑view consistency, robustness, and inference speed.
arXiv Computer Vision
Aug 27
LaGen: Towards Autoregressive LiDAR Scene Generation
LaGen is an autoregressive framework that generates long‑horizon LiDAR scenes frame by frame, using a single‑frame input and bounding‑box conditions to produce high‑fidelity 4D scenes. It introduces a scene decoupling estimation module for better object‑level interaction and a noise modulation module to reduce error accumulation over time. Evaluations on the nuScenes dataset show that LaGen outperforms existing methods, especially on later frames.
Hugging Face Trending Papers
Jun 2
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving policy model actively interacts with the environment, where its actions dynamically update the simulator state and directly influence the next set of generated sensor observations.
arXiv Computer Vision
Sep 1
HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving
arXiv:2511.22187v4 Announce Type: replace Abstract: Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support no...
arXiv AI
Jun 3
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
arXiv:2606. 03159v1 Announce Type: cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck.
arXiv AI
Jul 28
AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models
arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.