Two numbers are a whole face — drag the point, meet the decoder
The square on the left is a 2D latent space. Every point in it is a compressed description of an image: here, the x-axis means roundness and the y-axis means size. The decoder on the right expands those two numbers back into a full picture. Drag the gold point anywhere — the face is redrawn from scratch on every frame, purely from (x, y).
The decoder here is hand-coded (two numbers → drawing rules) so you can see the idea clearly. In a real model, a neural network learns this mapping from millions of images — and its latent space has hundreds of dimensions, not two.
Walking through latent space — morphing is just moving
Pick two points, A and B, and slide between them. There is no "morph algorithm" anywhere — the smooth transformation you see is nothing but the decoder being fed points along a straight line. If the space is well organized, every step of the walk is itself a valid image.
Every in-between face is decoded live from the interpolated coordinates — nothing is blended in pixel space.
Good neighborhoods — nearby points, nearby faces
A healthy latent space has two properties you can check by eye: neighbors look alike, and every direction means something (right = rounder, up = bigger — everywhere, consistently). Below is a 5×5 grid of decoded points around a center. Widen the neighborhood and watch variety grow smoothly instead of jumping randomly. Drag on the mini-map to move the whole neighborhood.
Similarity is computed live: the average difference in decoded properties (roundness, size) between each neighbor and the center. Points beyond the edge of the space are clamped.
The holes — wander into the regions that decode to garbage
Nobody promises the whole space is meaningful. If the encoder only ever placed training images in some regions, the decoder never learned what the gaps mean — sample a point there and you get junk. The hatched red zones below are such dead zones. Drag the point around: inside them, the decoder produces noise instead of a face.
Dead-zone shapes are illustrative, but the coverage percentage is measured live by probing a 40×40 grid of latent points against the zones.
Entangled knobs — when one dial secretly moves two things
Ideally each latent dimension controls one property. But nothing forces a model to learn axes that line up with human concepts — often a direction changes several things at once. Same two knobs, two decoders: in the entangled one the axes are rotated, so knob 1 drags roundness and size together. Move a knob in each mode and read the measured change.
The "entangled" decoder is the same decoder with its latent axes rotated 38° — a faithful stand-in for how real models mix concepts across dimensions.