arXiv Computer Vision
PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 9
WorldCast: Distributed Multiplayer World Models
WorldCast introduces a distributed multiplayer world model where each player runs a local client that generates video and estimates player position from recorded data. Clients share only player and scene states, allowing them to project these states into camera-aligned fields that guide rendering of other players. This approach achieves real‑time performance, improves rendering rates by over an order of magnitude, and maintains visual consistency across views without a centralized computational bottleneck.
arXiv AI
Jul 7
Multiplayer Interactive World Models with Representation Autoencoders
arXiv:2607. 05352v1 Announce Type: cross Abstract: We introduce the first multiplayer world model for highly dynamic environments governed by complex physical interactions.
arXiv Computer Vision
Oct 7
Artemis: Geometry-Grounded Multi-Agent Driving World Models with Shared 3D State and Progressive Memory Update
arXiv:2610.07031v1 Announce Type: new Abstract: Recent video world models have witnessed the paradigm shift from single-agent to multi-agent involvements, which can reveal more complicated dynamics a...
arXiv Computer Vision
Aug 25
WorldMind: Decoupled Game World Model for State-Aware NPC Behavior
WorldMind is a decoupled framework for state-aware NPC behavior in game world models, separating interactive world modeling into four layers: Understanding, Decision, Control, and Generation. It constructs a compact state from generated frames, reasons over it to plan NPC actions, translates actions into temporally aligned conditions, and synthesizes visual outcomes. Experiments on the newly introduced BOSS-140K dataset show that WorldMind achieves more tactically appropriate and coherent NPC behavior than baseline models in about 70% of pairwise comparisons.
arXiv AI
Jul 22
AlayaWorld: Interactive Long-Horizon World Modeling -- Full Technical Report
arXiv:2607. 18367v1 Announce Type: new Abstract: Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and programming, video world models generate interactive environments from user inputs instantly.
arXiv AI
Jun 3
MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data
arXiv:2606. 02753v1 Announce Type: cross Abstract: Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a single perspective.