Oneira: From Open-Ended Generation to Open-World Interaction in Video World Models

arXiv:2610.01614 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/MiniMax-AI/MiniMax-H3

Project page: https://madaoer.github.io/projects/oneira

Terminology

• World Model — 41× in this paper · explained in 7 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Grounding — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• State Management — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
• MASS: Multiplayer World Models with Authoritative Shared State
• Geometric Context Transformer for Streaming 3D Reconstruction
• World Narrative Model for Highly Controllable Video Generation: A Paradigm Shift from Pixel Sampling to Physical World Orchestration
• Code World Model: Coding Agent as World Brain
• Infinite Worlds with Versatile Interactions
• Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes
• MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
• World Models
• Mastering Diverse Domains through World Models
• Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
• RELIC: Interactive Video World Model with Long-Horizon Memory
• LoRA: Low-Rank Adaptation of Large Language Models
• Generative World Renderer
• DreamGen: Unlocking Generalization in Robot Learning through Video World Models
• ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
• Sekai: A Video Dataset towards World Exploration
• StatePlay: State-Aware Game World Models for Mechanics-Consistent Generation
• WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling

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