i-Design: Step-by-Step Graphic Layout Design with Progressive Aesthetic Policy Optimization

Automated layout generation powers a wide range of designs, such as posters, banners, and user interfaces. However, current single-shot generators overlook the iterative nature of human design and often produce aesthetically subpar compositions. We present i-Design, a unified framework that models layout generation as a progressive process, placing k elements at a time, conditioned on previously rendered partial canvases. Our approach combines Progressive Imitation Learning (PIL) on partial canvases, followed by Progressive Aesthetic Policy Optimization (PAPO), a reinforcement learning algorithm that aligns generation with aesthetic preferences. In PAPO, a frozen vision judge evaluates rendered layout pairs, constructs a directed comparison graph, and computes a global aesthetic consensus to derive trajectory-level rewards reflective of visual appeal rather than spatial accuracy. During training, multiple rollouts are rendered through a non-differentiable Skia engine and scored in real time by the judge to enable adaptive aesthetic learning. Experiments on Crello and WebUI benchmarks demonstrate that i-Design surpasses SoTA methods, achieving a 13% improvement in win rate and a 7% gain in mIoU, producing layouts with superior balance, alignment, and visual harmony. Our findings demonstrate that iterative generation and progressive aesthetic optimization offer a powerful new paradigm for human-aligned graphic layout generation.