Summary

This repository serves as a comprehensive, curated list of resources related to "World Models" in artificial intelligence. It aims to consolidate research across diverse domains including Embodied AI, Autonomous Driving, Natural Language Processing, and Agents, acting as a one-stop reference for researchers, practitioners, and enthusiasts. The project's benefit lies in organizing a rapidly growing and interdisciplinary field, providing a map of its applications and bridging different community perspectives.

How It Works

The project functions as a meticulously organized collection of papers, surveys, and foundational models related to world modeling. It categorizes resources by application domain (e.g., Game Simulation, Autonomous Driving, Embodied AI, Science) and by general approaches (e.g., 2D Vision Priors, 3D Vision Priors, Language Priors, Latent Space Models). This structure facilitates navigation and discovery of relevant work, aiming to track the latest breakthroughs and provide a unified view of the field.

Quick Start & Requirements

This is a curated list of research resources and does not involve software installation or execution. There are no direct installation commands, prerequisites, or setup requirements. Links to external resources, papers, and code repositories are provided within the list itself.

Highlighted Details

• Broad Scope: Covers world models across Embodied AI, Autonomous Driving, NLP, Game Simulation, and Scientific applications.
• Categorization: Organizes resources by application domain, theoretical approach (e.g., 2D/3D vision, language priors, latent space), and evaluation benchmarks.
• Resource Depth: Includes surveys, foundational models, and specific research papers with links to their respective sources.
• Community Driven: Actively welcomes contributions via Pull Requests to maintain and expand the resource.

Maintenance & Community

The project encourages community contributions via Pull Requests and provides an email for contact. It explicitly states "PRs Welcome" and "Community Contributions Welcome." Links to community platforms like Discord or Slack are not present in the README.

Licensing & Compatibility

The repository is licensed under CC0 1.0 Universal (Public Domain Dedication). This license allows for unrestricted use, modification, and distribution, making it highly compatible for both academic and commercial purposes without copyleft restrictions.

Limitations & Caveats

The repository creator notes a lack of deep expertise in autonomous driving, resulting in a less organized section for that domain, with an anticipation of community efforts to improve it. As a curated list, it reflects the current state of research and is continuously updated, meaning specific entries may become dated as the field evolves.