Reverie: A Gaussian Splat Study

Reverie reconstructs 23 looks from the 2023 LCF MA graduate show into volumetric three-dimensional space from a single fixed camera angle. Each frame of the original runway footage is processed individually through a monocular Gaussian splatting inference model, rebuilt in three dimensions, then reassembled into a continuous spatial sequence by a bespoke pipeline built entirely from scratch. The output is a study where garments and figures exist as point clouds: bodies that materialise out of particles, move through space, and dissolve into themselves on exit. Painterly, sculptural, and built entirely from what was already there.

Using monocular image synthesis and a bespoke pipeline built from scratch, Reverie investigates how found footage can be transformed into new forms of three-dimensional spatial expression. Runway looks are broken down frame by frame, volumetrically rebuilt through AI inference, and reassembled into a continuous sequence where garments and figures exist as sculpted point clouds. Through extensive custom scripting and post-production, the project reconciles the fundamental instability of per-frame reconstruction into a coherent moving study. Reverie explores how emerging spatial rendering techniques can represent fashion in three dimensions without modelling, simulation, or recreation from scratch.