MaskPLAN: Masked Generative Layout Planning from Partial Input
Hang Zhang, Anton Savov, Benjamin Dillenburger
Abstract
Layout planning spanning from architecture to interior design is a slow iterative exploration of ill-defined problems adopting a "I'll know it when I see it" approach to potential solutions. Recent advances in generative models promise automating layout generation yet often overlook the crucial role of user-guided iteration cannot generate full solutions from incomplete design ideas and do not learn for the inter-dependency of layout attributes. To address these limitations we propose MaskPLAN a novel generative model based on Graph-structured Dynamic Masked Autoencoders (GDMAE) featuring five transformers generating a blend of graph-based and image-based layout attributes. MaskPLAN lets users generate and adjust layouts with partial attribute definitions create alternatives for preferences and practice new composition-driven or functionality-driven workflows. Through cross-attribute learning and the user input as a global conditional prior we ensure that design synthesis is calibrated at every intermediate stage maintaining its feasibility and practicality. Extensive evaluations show MaskPLAN's superior performance over existing methods across multiple metrics.
BibTeX
@inproceedings{cvpr2024_maskplanmaskedge,
title = {MaskPLAN: Masked Generative Layout Planning from Partial Input},
author = {Hang Zhang and Anton Savov and Benjamin Dillenburger},
booktitle = {CVPR 2024},
year = {2024}
}