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Related Experiment Video

Updated: May 7, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

Simulating the Real World: A Unified Survey of Multimodal Generative Models.

Yuqi Hu, Longguang Wang, Xian Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 5, 2026
    PubMed
    Summary

    This survey unifies multimodal generative models for real-world simulation, progressing from 2D to 4D data representations. It bridges the gap in understanding data dimensionality for Artificial General Intelligence (AGI) research.

    Related Experiment Videos

    Last Updated: May 7, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    3.1K

    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Generative Models

    Background:

    • Replicating the real world is crucial for Artificial General Intelligence (AGI).
    • Current world models often treat different data dimensions (2D, video, 3D, 4D) independently.
    • Existing methods lack systematic integration of interdependencies across data modalities.

    Purpose of the Study:

    • To present a unified survey of multimodal generative models for real-world simulation.
    • To investigate the progression of data dimensionality from 2D to 4D generation.
    • To systematically integrate the study of 2D, video, 3D, and 4D generation within a single framework.

    Main Methods:

    • Reviewing multimodal generative models across increasing data dimensionality.
    • Analyzing the progression from 2D (appearance) to video (appearance+dynamics), 3D (appearance+geometry), and 4D (all dimensions) generation.
    • Providing a comprehensive overview of datasets, evaluation metrics, and future research directions.

    Main Results:

    • This is the first systematic unification of 2D, video, 3D, and 4D generation studies.
    • Identifies the progression and interdependencies of data dimensionality in generative models.
    • Offers insights for newcomers and guides future research in the field.

    Conclusions:

    • A unified framework for multimodal generative models is essential for advancing real-world simulation in AGI.
    • Systematic integration of data dimensionality enhances understanding and replication of the physical world.
    • This survey provides a foundational resource for future research in multimodal generation and AGI.