Reimagine Video Dynamics

arXiv:2609.36496 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://yuyuanspace.com/RVD

Terminology

• Benchmark — 14× in this paper · explained in 2 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Two-stage training — 7× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Reimagine Video Dynamics — 3× in this paper
• Two-Stage Training Strategy — 3× in this paper · explained in 3 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Qwen3-VL Technical Report
• VINO: A Unified Visual Generator with Interleaved OmniModal Context
• Seedance 1.0: Exploring the Boundaries of Video Generation Models
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
• DINOv2: Learning Robust Visual Features without Supervision
• Wan: Open and Advanced Large-Scale Video Generative Models
• Qwen-Image Technical Report
• Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
• OptiWorld: Optimal Control for Video World Generation under Physical Constraints

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