CLASS: Conditional Latent Architecture for Search and Synthesis of Design Layouts

Dipu Manandhar, Paul Guerrero, Zhaowen Wang, John Collomosse; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 5520-5529

We propose CLASS; a novel unified model for the synthesis and search for design layouts two tasks that are often handled separately by prior works. We propose to learn a compact and coherent latent feature of a layout supporting joint search and synthesis. This allows various operations such style-conditioned layout generation latent space manipulation and provides seamless integration of search and synthesis for an effective design workflow. We train CLASS with a dual decoder: a new transformer-based layout-conditioned decoder and a CNN-based raster decoder. The latent-conditioned decoder explicitly conditions upon a latent vector while generating a layout in an auto-regressive fashion. We train CLASS under variational framework which in conjunction with a raster-decoder enhances the latent representation improving both generation and retrieval performances. We show the effectiveness of CLASS on the RICO and PubLayNet benchmarks and demonstrate that CLASS is capable of high-quality synthesis from scratch as well as performing self-completion interpolation project between design layouts whilst achieving close to or better than state-of-the-art search performance.