ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps:
building footprint massing
Each of these three steps uses a TensorFlow Pix2Pix GAN-model (Christopher Hesse’s implementation) in the back-end, and their combination makes for a entire apartment building “generation stack” — according to Stanislas — which also allows for user input at each step.
The design of a building can be inferred from the piece of land it stands on. Hence, Stanislas fed his first model using GIS-data (Geographic Information System) from the city of Boston in order to generate typical footprints based on parcel shapes.
Stanislas’ second model was responsible for repartition and fenestration (the placement of windows and doors). This GAN took the footprint of the building (the output of model I) as input, along with the position of the entrance door (green square), and the positions of the user-specified windows.
Stanislas used a database of 800+ plans of apartments for training. To visualize the output, rooms are color-coded and walls and fenestration are blackened.
Finally, in the third model, the rooms are filled with appropriate furniture. What training data Stanislas has used here, he did not specify in the original blog.
Stanislas’ GAN-models progressively learned to design rooms and realistically position doors and windows. It took about 250 iterations to get some realistic floorplans out of the algorithm. Here’s how an example learning sequence looked like:
Now, Stanislas was not done yet. He also scaled the utilization of GANs to design whole apartment buildings. Here, he chains the models and processes multiple units as single images at each step.
Stanislas did other cool things to improve the flexibility of his ArchiGAN models, about which you can read more in the original blog. Let these visuals entice you to read more:
I believe a statistical approach to design conception will shape AI’s potential for Architecture. This approach is less deterministic and more holistic in character. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process represents a paradigm shift.