EPIC: Epipolar-Consistent 360 Immersive Stereo Video Generation

arXiv:2609.38689 · cs.CV, cs.HC · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.HC

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Consistency — 14× in this paper · explained in 3 episodes
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Rectified Flow — 4× in this paper · explained in 3 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Panoramic Epipolar Geometry Score — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Temporal consistency — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Steering — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Training Diffusion Models with Reinforcement Learning
• Directly Fine-Tuning Diffusion Models on Differentiable Rewards
• World Reconstruction From Inconsistent Views
• Pyramidal Flow Matching for Efficient Video Generative Modeling
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• Epipolar Geometry Improves Video Generation Models
• CubeComposer: Spatio-Temporal Autoregressive 4K 360{\deg} Video Generation from Perspective Video
• Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
• Flow Matching for Generative Modeling
• Improving Video Generation with Human Feedback
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation
• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
• Movie Gen: A Cast of Media Foundation Models
• Aligning Text-to-Image Diffusion Models with Reward Backpropagation
• Stereo World Model: Camera-Guided Stereo Video Generation
• Wan: Open and Advanced Large-Scale Video Generative Models
• PanoWan: Lifting Diffusion Video Generation Models to 360{\deg} with Latitude/Longitude-aware Mechanisms
• StereoWorld: Geometry-Aware Monocular-to-Stereo Video Generation
• VIGOR: VIdeo Geometry-Oriented Reward for Temporal Generative Alignment

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