CoDrive: Cross-Vehicle World-Consistent Video Generation with Precise Trajectory Control for Cooperative Driving

arXiv:2609.34749 · cs.CV, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://codrive-project-page.github.io

Terminology

• Consistency — 74× in this paper · explained in 3 episodes
• World Model — 41× in this paper · explained in 7 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Global Camera Geometry Injection — 5× in this paper
• Actor — 4× in this paper · explained in 3 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Chamfer Distance — 4× in this paper
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Average Displacement Error — 3× in this paper
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Dynamic Time Warping — 3× in this paper
• Dynamic Time Warping (DTW) — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Relative Reprojection Depth Error — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• D Localization Error — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• BoT-SORT: Robust Associations Multi-Pedestrian Tracking
• UniMLVG: Unified Framework for Multi-view Long Video Generation with Comprehensive Control Capabilities for Autonomous Driving
• Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
• Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
• GAIA-1: A Generative World Model for Autonomous Driving
• Multiplayer Interactive World Models with Representation Autoencoders
• MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data
• DiVE: DiT-based Video Generation with Enhanced Control
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• OmniNWM: Omniscient Driving Navigation World Models
• DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model
• Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players
• Yume-1.5: A Text-Controlled Interactive World Generation Model
• Cosmos 3: Omnimodal World Models for Physical AI
• UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
• ActionParty: Multi-Subject Action Binding in Generative Video Games
• Solaris: Building a Multiplayer Video World Model in Minecraft
• Prisma-World: Camera-Controllable Multi-Agent Video World Model
• V2VCrafter: Consistent Street-View Image Generation Across Vehicles
• Wan: Open and Advanced Large-Scale Video Generative Models

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