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arXiv · 2605.06170

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

Juntong Wang·Jiarui Wang·Huiyu Duan·Lewei Li·Guangtao Zhai·Xiongkuo Min
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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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Juntong Wang, Jiarui Wang, Huiyu Duan, Lewei Li, Guangtao Zhai, Xiongkuo Min. 2026-05-07. DynT2I-Eval: A Dynamic Evaluation Framework for Text-to-Image Models. https://arxiv.org/abs/2605.06170

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