World Observer: Joint Actor-Observer Generation for Persistent World Modeling

arXiv:2610.02162 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Project page: https://cvlab-kaist.github.io/world-observer

Terminology

• Actor — 197× in this paper · explained in 3 episodes
• World Model — 34× in this paper · explained in 7 episodes
• Benchmark — 28× in this paper · explained in 2 episodes
• Fidelity — 14× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Embeddings — 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
• Adaptation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• APEX — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Digital Twin — 1× in this paper · explained in 4 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• SAM 3: Segment Anything with Concepts
• 360+x: A Panoptic Multi-modal Scene Understanding Dataset
• MemoBench: Benchmarking World Modeling in Dynamically Changing Environments
• Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
• Pantheon360: Taming Digital Twin Generation via 3D-Aware 360{\deg} Video Diffusion
• DreamX-World 1.0: A General-Purpose Interactive World Model
• LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion
• VBench: Comprehensive Benchmark Suite for Video Generative Models
• PanoWorld: Geometry-Consistent Panoramic Video World Modeling
• CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• OmniNWM: Omniscient Driving Navigation World Models
• MagicWorld: Towards Long-Horizon Stability for Interactive Video World Exploration
• PanoWorld: Real-World Panoramic Generation
• CubeComposer: Spatio-Temporal Autoregressive 4K 360{\deg} Video Generation from Perspective Video
• Depth Anything 3: Recovering the Visual Space from Any Views
• Flow Matching for Generative Modeling
• OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation

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