Action Forcing: Training World Models on Unsupervised Video by Recovering Underlying Egomotion Bases

arXiv:2609.30595 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Ablation — 37× in this paper · explained in 1 episode
• Steering — 25× in this paper · explained in 2 episodes
• World Model — 21× in this paper · explained in 7 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• AUC — 13× in this paper · explained in 2 episodes
• Action Space — 9× in this paper · explained in 1 episode
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Optical Flow — 3× in this paper · explained in 2 episodes
• Principal Components Analysis — 3× in this paper
• CVF International Conference on Computer Vision — 2× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 7 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Failure Modes — 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
• Generative Models — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• World Simulation with Video Foundation Models for Physical AI
• Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight
• X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
• Olaf-World: Orienting Latent Actions for Video World Modeling
• MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft
• AdaWorld: Learning Adaptable World Models with Latent Actions
• RELIC: Interactive Video World Model with Long-Horizon Memory
• minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models
• WorldCam: Interactive Autoregressive 3D Gaming Worlds with Camera Pose as a Unifying Geometric Representation
• Astra: General Interactive World Model with Autoregressive Denoising
• Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
• Yume: An Interactive World Generation Model
• Cosmos World Foundation Model Platform for Physical AI
• Vid2World: Crafting Video Diffusion Models to Interactive World Models
• Qwen3-VL Technical Report
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

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