arXiv Computer Vision
GameVerse: A Minute-Scale Gameplay Dataset for Long-Horizon Interactive World Modeling
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
Aug 26
Game2World Engine: Unlocking In-the-Wild Gameplay Videos for World Model Training
arXiv:2608.24680v1 Announce Type: new Abstract: Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the...
arXiv Computer Vision
Sep 22
GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay
arXiv:2609.25001v1 Announce Type: new Abstract: Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, a...
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 Computer Vision
1d ago
GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models
GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models presents GEST-Engine, an open‑source system that deterministically renders multi‑actor video scenarios while recording exhaustive ground truth—including 3D entity and camera states, pairwise spatial relations, event‑to‑frame mappings, instance segmentation, and long descriptions—at no extra annotation cost. The authors release a 938‑video dataset, GTASA, which offers the densest spatial‑relation coverage of any video dataset, with an entity‑pair relation graph at every frame that is roughly 84× denser than current state‑of‑the‑art collections. They demonstrate the dataset’s value by showing that pretraining on GTASA improves VideoLLaMA3 captioning, and by using it to probe frozen video encoders on 11 spatio‑temporal tasks, revealing that relational understanding remains near chance. whyItMatters":"The dataset provides a uniquely dense, exact 3D ground truth for spatio‑temporal video analysis, enabling measurable progress in training and evaluating video models on relational reasoning tasks."
arXiv Computer Vision
Sep 30
Matrix-game 2.0: An open-source, real-time, and streaming interactive world model
arXiv:2508.13009v5 Announce Type: replace Abstract: Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynami...
arXiv Computer Vision
Oct 15
PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents
arXiv:2610.20240v1 Announce Type: new Abstract: A multiplayer world model must predict how the independent actions of several players change a shared environment, including changes outside a player's...