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A machine learning approach where colors are viewed not as points in a simple cube, but as lying on a complex curved surface (manifold). Distance along the manifold produces more natural color transitions than straight-line distance.
Traditional color science treats colors as points in a 3D cube (RGB) or cylinder (HSL). Manifold learning takes a different approach: it considers colors as lying on a complex, curved surface — a manifold — embedded in higher-dimensional space. The "true" distance between two colors is measured along this surface, not through the cube.
Imagine the surface of the Earth. The straight-line (Euclidean) distance between London and Tokyo goes through the planet, but the meaningful distance follows the curved surface. Similarly, the perceptually meaningful path between two colors may curve through color space rather than cut straight through it. This curved path distance is called a geodesic.
When generating smooth color transitions (gradients), interpolating linearly in RGB or even OKLCH can produce desaturated midpoints. Manifold-aware interpolation follows the curved surface of perceptually valid colors, producing transitions that feel more natural. This is particularly important for generative art that traverses large hue ranges — a gradient from red to blue that stays vivid rather than passing through muddy gray.
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