One Design, Many Canvases: Synthesizing Layout Pairs to Learn Layered Design Adaptation

Adapting layered graphic designs to different canvases requires rearranging layered elements while preserving their grouping structure, reading order, and relative sizes. Beyond scaling and repositioning, different element types also require specific adaptations: text may reflow, while photos may be cropped. Learning such layout adaptation is challenging because paired layered designs containing the same elements across different canvas sizes are scarce. We address this limitation with a scalable layout-pair synthesis pipeline that, given a source design and target canvas size, generates an adapted target image and then recovers its layout using a trained VLM. Using this pipeline, we construct ReCanvas-Data, a dataset of 4.22M source–target layout pairs derived from 292K layered designs spanning posters, presentation slides, and infographics. To learn source-to-target adaptation from ReCanvas-Data, we develop ReCanvas, a vision-language model based on our proposed Layout-Structure Chain-of-Thought (LS-CoT) method. LS-CoT first infers a scene tree that captures the nested grouping and reading order of elements in the source design, and then conditions target-layout prediction on the inferred tree. Experiments show that ReCanvas outperforms baselines in layout quality while more faithfully preserving the source layout structure across canvas sizes.