Kyle Steinfeld

Artificialis Releivo

Artificial Relief

Kyle Steinfeld, Titus Ebbecke, Georgios Grigoriadis, & David Zhou

A dataset of fragmented and decontextualized Greco-Roman sculptural relief underlies the generation of uncanny forms that straddle the unrecognizable and the familiar. Samples include those drawn from the Pergamon Altar: a Greek construction originating in modern-day Turkey, disassembled in the late 19th century, and re-assembled in the early 20th century in a Berlin museum. The project operates similarly. It begins with a disassembly of selected sculptural forms into fragments that can be described as deformations of a flat sheet. Where ML processes often struggle to describe three-dimensional form, these "vector displacement maps" are comprehensible to the machine, and serve to train a neural network - a gently modified implementation of StyleGAN - to understand the form-language of the selected source material. Recalling the rhythmic symmetry of frieze patterns found in traditional Western ornament, a "walk" through the latent space of Greco-Roman sculptural forms is aggregated across a surface in high relief.

Intelligence Artificielle & Architecture

Kyle Steinfeld

Some of my work was featured in the 2020 book Intelligence Artificielle & Architecture (Artificial Intellengence and Architectrue), by Stanislas Chaillou.

Intelligence Artificielle & Architecture Exhibit

Kyle Steinfeld

I displayed works at the Intelligence Artificielle & Architecture exhibit at the Pavillon De l'Arsenal in Paris. This exhibit "takes us through the main stages of an evolution that started from the studies on Modularity, Computer-aided Design (CAD), Parametrics and, finally, Artificial Intelligence", and was curated by Stanislas Chaillou.

Death Valley

NeurIPS 2017 Machine Learning for Creativity and Design

Kyle Steinfeld

In work exhibited at the NeurIPS 2017 Machine Learning for Creativity and Design, we developed a process for relating depthmaps extracted from Google Street View panoramas with the corresponding photographic information. A number of separate models were produced using limited geographic areas of selected cites. With these depthmap-to-panoramic cityscape models trained, we are able to generate new images from unrelated depthmaps which resembled photographic images of the selected cities. This is demonstrated using a javascript app (not currently online) and documented in still images and a series of videos.

GAN Loci - ACADIA 2019

Imaging Place using Generative Adversarial Networks

Kyle Steinfeld

This paper proposes the production of synthetic images of cities using generative adversarial networks (or GANs) represents the first computational approach to documenting the Genius Loci of a city, which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN and Pix2Pix) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images.

GAN Loci - Towards Data Science

Kyle Steinfeld

This post on the Towards Data Science blog concisely summarizes the GAN Loci project - a project which applies techniques in machine learning in order to produce synthetic images intended to capture the predominant visual properties of urban places.