[ACM MM 2025] Official PyTorch Code for "Uni-Layout: Integrating Human Feedback in Unified Layout Generation and Evaluation"
Layout generation plays a crucial role in enhancing both user experience and design efficiency. However, current approaches suffer from task-specific generation capabilities and perceptually misaligned evaluation metrics, leading to limited applicability and ineffective measurement. In this paper, we propose \textit{Uni-Layout}, a novel framework that achieves unified generation, human-mimicking evaluation and alignment between the two. For universal generation, we incorporate various layout tasks into a single taxonomy and develop a unified generator that handles background or element contents constrained tasks via natural language prompts. To introduce human feedback for the effective evaluation of layouts, we build \textit{Layout-HF100k}, the first large-scale human feedback dataset with 100,000 expertly annotated layouts. Based on \textit{Layout-HF100k}, we introduce a human-mimicking evaluator that integrates visual and geometric information, employing a Chain-of-Thought mechanism to conduct qualitative assessments alongside a confidence estimation module to yield quantitative measurements. For better alignment between the generator and the evaluator, we integrate them into a cohesive system by adopting Dynamic-Margin Preference Optimization (DMPO), which dynamically adjusts margins based on preference strength to better align with human judgments. Extensive experiments show that \textit{Uni-Layout} significantly outperforms both task-specific and general-purpose methods.
[2025-09-02]: 🚀 CoT data has been released! You can now find it in the "Dataset for Reward Model" link.
[2025-08-04]: 🎯 Our paper is now available on arXiv! Check it out here: https://arxiv.org/abs/2508.02374.
[2025-07-04]: 🎉 Exciting news! Our paper has been accepted to ACM MM 2025! Stay tuned for more updates!
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