# Real-Time Style Transfer

**Track:** Creative ML & AI-in-the-Loop — Advanced Creative Coding — proposed (50)
**Framework / surface:** p5.js
**Level:** Medium
**Prerequisites:** Images & Pixels as Arrays, Run a Model Client-Side

**In one line:** Fast arbitrary neural style transfer on a webcam feed.

## Theory, aesthetics & inspiration

Style transfer rests on a 2015 insight from Leon Gatys, Alexander Ecker, and Matthias Bethge: in a convolutional network, content lives in feature activations while style lives in their Gram-matrix correlations, and the two can be optimized apart. The original was slow; feed-forward distillation (Johnson and colleagues, 2016) and adaptive instance normalization (Huang and Belongie, 2017) compressed it to a single pass, fast enough for a webcam. The result is a painterly filter that respects structure—a live feed rendered continuously in another work's hand. ml5.js ships browser-ready style models, making the medium itself adjustable in real time rather than a post-process applied once.