An Algorithmic Gaze

The machine was trained on human bodies — and what it learned says more about us than about it.

A generative AI work that makes visible what a model has absorbed from vast collections of body imagery. Visitors encounter a continuous stream of human figures that shift across sex, identity, and form. Produced not by an artist’s hand, but by the statistical patterns a neural network extracted from its training data.

We built the system around two containerized services that cover the full pipeline from training to exhibition. The training service wraps StyleGAN3 in a REST API, managing the entire model lifecycle. New training runs are assigned unique identifiers, checkpoints are saved at regular intervals, and runs can be resumed from any previous snapshot. Quality metrics are tracked throughout training, giving us a quantitative read on how the model is developing over thousands of iterations.

Before training begins, datasets are preprocessed. Images are resized, centred, and filtered. Decisions that shape what distribution the model will generalise from. Those preprocessing choices are as consequential as anything that happens during training itself.

The inference service handles generation and output. Images are produced by sampling from the model’s learned latent space, and morphs between outputs are produced by interpolating through that same space moving gradually from one point to another in a geometry the model constructed entirely from data. For the Wellcome Collection run, a separate load-balancing service distributed generation jobs across multiple inference endpoints, producing continuous video across every open hour of the exhibition’s twenty-six-week run.

Part of the permanent collection at TRAPHOLT Museum, Denmark. Also shown at the Wellcome Collection, London.