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  3. Interactive Visual Assessment For Text-to-image Generation Models.

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Interactive Visual Assessment for Text-to-Image Generation Models.

Xiaoyue Mi, Fan Tang, Juan Cao

    IEEE Transactions on Visualization and Computer Graphics
    |January 28, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    DyEval, a new framework, enhances visual generation model assessment by using LLMs for dynamic, interactive evaluation. It helps identify more failures and complex patterns than traditional methods.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Visual generation models show progress but face real-world deployment challenges.
    • Current assessment methods are limited by fixed coverage, evolving difficulty, and data leakage.
    • Complex generation models require more effective evaluation strategies.

    Purpose of the Study:

    • To propose DyEval, a dynamic, interactive visual assessment framework for text-to-image systems.
    • To address limitations of traditional isolated assessment frameworks.
    • To improve the comprehensive evaluation of visual generation models.

    Main Methods:

    • DyEval utilizes a Large Language Model (LLM)-powered framework for collaborative human-AI evaluation.
    • An intuitive interface allows interactive exploration and analysis of model behaviors.
  • Adaptive generation of diverse textual inputs probes model capability boundaries based on user feedback.
  • A contextual reflection module analyzes failure triggers and patterns using LLM reasoning.

    • Main Results:

    • DyEval identified up to 2.56 times more generation failures compared to conventional methods.
    • The framework uncovered complex and rare failure patterns, including pronoun and cultural context generation issues.
    • Qualitative and quantitative experiments validated DyEval's effectiveness.

    Conclusions:

    • DyEval offers a novel approach to visual generation model assessment, enhancing reliability.
    • The framework provides valuable insights for improving generative models.
    • DyEval has broad implications for advancing visual generation systems across domains.