Building Facade Design through Paired Image-to-Image Translation using Pix2Pix

Abstract

This project leverages the pix2pix image-to-image translation framework to generate realistic building facades from semantic label maps using the CMP Facade dataset. The primary objective was to reproduce the original conditional GAN architecture and validate its performance on the facade-generation task. Beyond model reproduction, we developed a lightweight web-based interface using a standard JavaScript canvas that allows users to draw block-level layout sketches. These sketches are then processed by the trained model to generate corresponding facade images, providing a simpler and faster method for prototyping architectural facades compared to traditional manual sketching techniques. The interactive workflow preserves the structural and textural fidelity of the generated buildings, while making facade visualization more accessible and user-friendly. Experimental results demonstrate that the reproduced model effectively captures building features, and the interface offers a practical tool for rapid architectural concept development and visualization. Overall, this work presents an application-focused replication of pix2pix that bridges the gap between theoretical model reproduction and practical, interactive design workflows, highlighting its potential for supporting early-stage architectural prototyping and creative experimentation.