JEPA Guided Diffusion: Predictive Vision-Language Conditioning for Generative Traffic Forecasting

arXiv:2609.21379 · cs.CV · Submitted 2026-09-18 · Read on arXiv

cs.CV

Submitted: 2026-09-18

Updated: 2026-09-18

Code: https://github.com/AlterraFa/JEPAGuided-Diffusion

Terminology

• World Model — 13× in this paper · explained in 7 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Actor — 7× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Latent Representation — 7× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Latent Representations — 3× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Joint Embedding Predictive Architectures — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Structured Prompting — 1× in this paper · explained in 2 episodes

Sources

• Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
• DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving
• Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• Revisiting Feature Prediction for Learning Visual Representations from Video
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• GAIA-1: A Generative World Model for Autonomous Driving
• DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT
• MAD: Motion Appearance Decoupling for efficient Driving World Models
• Qwen3-VL Technical Report

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