Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark

Driven by the remarkable progress in diffusion models, text-to-image generation has achieved substantial advancements, underscoring the urgent need for robust automatic quality assessment. This task is inherently complex, requiring evaluations that range from object presence and attribute correctness to relational consistency and visual fidelity. Consequently, current state-of-the-art MLLM-based approaches often rely on powerful commercial models such as GPT-4o, which offer superior reasoning and instruction-following capabilities but are not universally accessible. In contrast, while open-source MLLMs demonstrate promising skills in vision and language understanding, they underperform in comprehensive image quality assessment. To address these challenges, we propose a task decomposition evaluation framework based on GPT-4o to automatically construct a specialized training dataset, breaking down the multifaceted evaluation process into simpler sub-tasks and thus reducing learning complexity. Building on this dataset, we design novel training strategies to distill GPT-4o's evaluation capabilities into a 7B open-source MLLM, MiniCPM-V-2.6, enabling it to better follow instructions across diverse assessment criteria. Furthermore, to reliably and comprehensively assess prior works and our proposed model, we manually annotate a meta-evaluation benchmark that includes chain-of-thought explanations alongside quality scores for generated images. Experimental results demonstrate that our distilled open-source MLLM significantly outperforms the current state-of-the-art GPT-4o-base baseline, VIEScore, with over 4.6% improvement in Spearman and Kendall correlations with human judgments.