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

We propose CLASS; a novel unified model for the syn-thesis 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 vari-ous operations such style-conditioned layout generation, la-tent 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 en-hances 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 synthe-sis from scratch, as well as performing self-completion, in-terpolation, project between design layouts, whilst achieving close to or better than state-of-the-art search performance.