HelloWorld: Towards Practical Applications of Generative Driving World Models

arXiv:2609.28931 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Project page: https://helloworld-4d.github.io

Terminology

• Consistency — 57× in this paper · explained in 3 episodes
• World Model — 34× in this paper · explained in 7 episodes
• Alignment — 25× in this paper · explained in 1 episode
• Benchmark — 21× in this paper · explained in 2 episodes
• Distillation — 13× in this paper · explained in 2 episodes
• Fidelity — 13× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Calibration — 10× in this paper · explained in 7 episodes
• Distribution Matching — 9× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Generative Model — 4× in this paper · explained in 2 episodes
• KV cache — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Alignment Scores — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 7 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Cross-camera Sampson Error — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Wan: Open and Advanced Large-Scale Video Generative Models
• Cosmos World Foundation Model Platform for Physical AI
• Advancing Open-source World Models
• BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout
• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
• MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control
• DiVE: DiT-based Video Generation with Enhanced Control
• Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
• GAIA-1: A Generative World Model for Autonomous Driving
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
• DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
• MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• Infinite Worlds with Versatile Interactions
• VACE: All-in-One Video Creation and Editing
• Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
• UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving
• Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving
• OmniNWM: Omniscient Driving Navigation World Models
• FAR-Drive: Frame-AutoRegressive Video Generation in Closed-Loop Autonomous Driving

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