# Latent Walks & Interpolation

**Track:** Creative ML & AI-in-the-Loop — Advanced Creative Coding — proposed (50)
**Framework / surface:** cross-framework
**Level:** Hard
**Prerequisites:** Latent Space as Coordinate Space, Embeddings & Vector Similarity

**In one line:** Traversing and interpolating latent space as animation.

## Theory, aesthetics & inspiration

A generative model learns a continuous space in which nearby points decode to similar outputs; to animate is to move through it. Linear paths drift, so practitioners interpolate on the hypersphere—Tom White's spherical sampling ("Sampling Generative Networks", 2016) named the technique—yielding morphs that feel inevitable rather than crossfaded. Specific directions encode attributes, turning navigation into a choreography of meaning. This terrain is what Mario Klingemann and Refik Anadol have mined for the uncanny in-between, and what Memo Akten's "Learning to See" framed as a machine projecting its memories onto the present moment. The frame is no longer drawn; it is located.