[2602.23438] DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23438 (cs) [Submitted on 26 Feb 2026] Title:DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation Authors:Varun Gopal, Rishabh Jain, Aradhya Mathur, Nikitha SR, Sohan Patnaik, Sudhir Yarram, Mayur Hemani, Balaji Krishnamurthy, Mausoom Sarkar View a PDF of the paper titled DesignSense: A Human Preference Dataset and Reward Modeling Framework for Graphic Layout Generation, by Varun Gopal and 8 other authors View PDF HTML (experimental) Abstract:Graphic layouts serve as an important and engaging medium for visual communication across different channels. While recent layout generation models have demonstrated impressive capabilities, they frequently fail to align with nuanced human aesthetic judgment. Existing preference datasets and reward models trained on text-to-image generation do not generalize to layout evaluation, where the spatial arrangement of identical elements determines quality. To address this critical gap, we introduce DesignSense-10k, a large-scale dataset of 10,235 human-annotated preference pairs for graphic layout evaluation. We propose a five-stage curation pipeline that generates visually coherent layout transformations across diverse aspect ratios, using semantic grouping, layout prediction, filtering, clustering, and VLM-based refinement to produce high-quality comparison pairs. Human preferences are annotated using a 4-class scheme (left,...