This repository is a curated literature review (awesome list) focused on the progression from video generation to world models in AI research. It systematically organizes and categorizes recent academic papers (primarily from 2022-2026) into a hierarchical structure covering technical foundations, application domains, evaluation methodologies, and theoretical perspectives. The main value lies in its comprehensive paper collection with structured metadata including publication dates, venues, paper links, project pages, and official code repositories. The repository follows a standard awesome-list format using markdown tables with shields.io badges for easy navigation to paper resources. Content is organized into eight main sections: Generation 1-4 (covering faithfulness, interactiveness, planning, and randomness aspects), Evaluation and Datasets, Study and Rethinking, Downstream Tasks, and World Models for Other Applications. Each section contains subsections detailing specific technical approaches (e.g., GAN-based, U-Net-based, DiT-based video generation) and application areas (robotics, autonomous driving, gaming). The list is actively maintained with frequent updates incorporating very recent pre-print papers. As a static reference resource, the repository contains no executable code, data processing pipelines, or computational components. Its reproducibility is high because the curated content is directly available in the README upon cloning, requiring no setup or dependencies to access the paper list. The organizational pattern demonstrates good practices for academic literature curation, though it does not contain novel reusable data engineering components beyond the standard markdown documentation format.

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