ZYT-World: A Real-Time Controllable World Model for Closed-Loop Autonomous-Driving Simulation

arXiv:2609.21712 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-22

Code: https://github.com/madebyollin/taehv

Terminology

• World Model — 23× in this paper · explained in 7 episodes
• Distillation — 17× in this paper · explained in 2 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Quantization — 8× in this paper · explained in 12 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Decode — 6× in this paper · explained in 2 episodes
• Distribution Matching — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Pruning — 4× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Gaussian Splatting — 3× in this paper · explained in 4 episodes
• Generative Models — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Alternation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Information Asymmetry — 1× in this paper · explained in 2 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
• Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
• Flex Attention: A Programming Model for Generating Optimized Attention Kernels
• One-Forcing: Towards Stable One-Step Autoregressive Video Generation
• Generative Adversarial Networks
• InfinityDrive: Breaking Time Limits in Driving World Models
• GAIA-1: A Generative World Model for Autonomous Driving
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
• AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
• FAR-Drive: Frame-AutoRegressive Video Generation in Closed-Loop Autonomous Driving
• Diffusion Adversarial Post-Training for One-Step Video Generation
• Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
• Fr'echet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering
• Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• Wan: Open and Advanced Large-Scale Video Generative Models

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