DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models

• URL: http://arxiv.org/abs/2605.06170v1
• Date: Thu, 07 May 2026 12:53:51 GMT
• Title: DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models
• Abstract summary: We propose DynT2I-Eval, a fully automated dynamic evaluation framework for text-to-image (T2I) models.<n>It constructs a structured visual semantic space from long-form descriptions, decomposing prompts into controllable dimensions.<n>DynT2I-Eval evaluates model performance across text alignment, perceptual quality, and aesthetics.
• Score: 78.62380562116135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing text-to-image (T2I) benchmarks largely rely on fixed prompt sets, leaving them vulnerable to overfitting and benchmark contamination once publicly released and repeatedly reused. In this work, we propose DynT2I-Eval, a fully automated dynamic evaluation framework for T2I models. It constructs a structured visual semantic space from long-form descriptions, decomposing prompts into controllable dimensions (e.g., subject, logical constraint, environment, and composition). This enables the continuous generation of fresh prompts via task-specific spaces and difficulty-aware sampling. DynT2I-Eval evaluates model performance across text alignment, perceptual quality, and aesthetics. Heterogeneous outputs are unified into prompt-conditioned pairwise comparisons, allowing a dynamic scheduler, micro-batch aggregation, and weighted Bayesian updates to maintain a stable online leaderboard despite changing prompt distributions and model injection. Experiments with independently sampled prompt streams demonstrate that continually refreshed prompts provide a robust evaluation protocol, reducing the impact of prompt-set-specific tuning. Simulations and ablations further confirm that the proposed ranking framework achieves a strong balance among cold-start convergence, late-entry discovery, and long-run ranking fidelity.

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