The Chair Project: A Case-Study for using
Generative Machine Learning as Automatism

Philipp Schmitt
School of Art, Media, and Technology
Parsons School of Design
New York, NY 10011
pschmitt@newschool.edu

Steffen Weiß
info@steffen-weiss.design

Abstract
We present a case study for the use of generative adversarial neural networks asautomatisms to stimulate (or augment) the imagination of human designers. We
generate evocative visual prompts for chair designs that become the basis for adesign process in which we create sketches, 3D models, and physical prototypes.

1Introduction

Images produced by generative adversarial neural networks (GANs) are frequently similar in theirvisceral evocative qualities, surreal creatures and objects. Thus, they sometimes remind of thepaintings and drawings of Surrealist artists like Max Ernst. The Surrealists, believing that “thecreativity that came from deep within a person’s subconscious could be more powerful and authentic
than any product of conscious thought,” [1] used so-called automatisms: generative art-making
techniques to stimulate the imagination.
The authors are not suggesting that GANs adhere to the Surrealist movement, but rather its techniques.
We have previously argued that GANs are capable of producing evocative visual prompts similar
to frottage — to name just one automatism. What if we employed this means of making images
neither as practical debugging nor as art in itself but rather as a tool for mind bending—like Surrealist
automatisms: one that caters to the subconscious, the associative, the imaginary rather than therational? [2]Taking the chair, the archetype of a designed object, as an example, we present The Chair Project:1 acase study for the use of GANs to augment human imagination.

2Design Process

2.1

Generative Models

We trained a DCGAN [3] using an original dataset consisting of 600 images of iconic 20th-centurychairs. The resulting model was used to generate new images of chairs. It was not our goal to generate
a functional piece of furniture, but to generate engaging, semi-abstract ‘visual prompts’ for a humandesigner who used them as a starting point for actual chair design concepts. This stands in contrast
to other procedural design approaches like Dreamsketch [4] which optimizes designs for functional
requirements, e.g. maximum stability at lowest weight.
Our neural network generated hundreds of chairs: Some are barely recognizable as furniture andmany others are not exactly functional, lacking a seat or missing a leg. There are concrete ones that
1

More information available at https://philippschmitt.com/work/chair.

32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

Figure 1: Steps from generated image to sketch to physical model
remind of specific iconic designs and others that seemingly fit into a style, an era or a manufacturing
process.
2.2

Physical Models

We turned a selection of generated chairs into sketches and, ultimately, concepts for real chairs (Fig.
1). The idea was to neither simply trace the generated images, nor to transform it into traditional
pieces of furniture. Rather, we brought out the chairs we saw in the blurry images to help viewers see
what we imagined. ‘Seeing the chair’ in an image is an imaginative and associative process. It pushes
designers away from usual threads of thinking towards unusual ideas that they might not have had
otherwise. Methods like sketching and 3D modeling introduce limitations and challenges that the AI
didn’t face (e.g. perspective) and create opportunities for imaginative thinking.
Based on the sketches, we crafted miniature prototypes of four designs. The physical world introduces
yet another layer of constraints (e.g. material properties and laws of physics) and consequently newchallenges: How to create this joint? Will it stand? Which tools do I need? How should previously
occluded parts look like?
2.3Conclusion

Before the 19th century a craftsman-designer, often a trained furniture maker, designed and built aunique chair themselves. In the industrial revolution design became separated from the production
process. Then, the industrial designer designed a chair using a creative process and a producer took
specifications to mass-produce thousands of identical chairs. In the late 20th century computer-aided
design (CAD) became an important tool to design machine-supported and optimized for machine
production. Today, materials and production methods have further diversified, yet the human is still
the creative agent whereas the machine is responsible for production.
Our work turns around the product design and production process, using machine learning notfor optimization or mass production but as an early step in the design process, to stimulate theimagination. We use machine learning to generate surprising, evocative prompts — something alien
a human might never come up with: as augmented imagination.
2

References[1] MoMA, Surrealism. Museum of Modern Art, https://www.moma.org/learn/moma_learning/themes/surrealism.
[2] Schmitt, P. (2018) Augmented Imagination: Machine Learning Art as Automatism. Plot(s), the DesignStudies Journal, Volume 5, pp. 25-32, New York, NY[3] Radford, A., Metz, L. and Chintala, S. (2015) Unsupervised representation learning with deep convolutional
generative adversarial networks. arXiv preprint arXiv:1511.06434.
[4] Kazi, R.H., Grossman, T., Cheong, H., Hashemi, A. and Fitzmaurice, G.W. (2017) DreamSketch: Early
Stage 3D Design Explorations with Sketching and Generative Design. In UIST (pp. 401-414).

3