Suppose you have a pair of handcuffs linked together. Can you pull them apart without unlocking or breaking them, or letting them pass through themselves? With real handcuffs, definitely not! But if they're made of stretchy rubber, it turns out to be possible—as shown here. 1/9
This motion provides a surprising example of what is known in mathematics as an "ambient isotopy" of two surfaces: a continuous motion where the surface is not ripped, cut, pinched, or allowed to pass through itself. 2/9
A more classic example is the "unknot problem": given a loop of string, can it be untangled into a circle without cutting? Even this simple question turns out to be very hard to answer in general. And only gets harder when you start thinking about surfaces rather than curves. 3/9
So how do you find an isotopy that unlinks the handcuffs? Over the years, people have made all sorts of drawings of this transformation—but they can be pretty hard to follow unless you already have a pretty good geometric imagination! 4/9
The unlinking motion in the video above was computed in a particularly remarkable way: it was not keyframed or manipulated by a human animator, but computed automatically by a program that does not do any high-level reasoning (or machine learning!). 5/9
Instead, it minimizes an energy that knows only about the shape of the surface at the current moment in time. Just like gravity makes a ball fall to the ground, this so-called tangent-point energy makes a surface "fall" into the configuration that maximizes self-separation. 6/9
To make the movie, we minimize the energy from both the "linked" and "unlinked" starting points. Then we reverse the second movie, and join them together. 7/9
By the way, this idea of connecting two surfaces—or other objects—through a canonical object is a powerful idea in mathematics, and also the starting point for a lot of computational algorithms (such as shape correspondence):
What's the nicest way to draw a shape with many "holes"?
We can use the principle of repulsion to explore this question: each point of the shape behaves like a charged particle, trying to repel all others. Surface tension prevents everything from shooting off to infinity. 1/n
For millennia people have been drawn to the question: what are the "nicest" possible shapes that exist?
This is really a basic question about nature: these shapes exist outside space and time; the same shapes can be discovered by civilizations anywhere in the universe. 2/n
"Nicest" could mean the most symmetric—for instance, the ancient Greeks discovered there were five so-called Platonic solids where every face and every vertex looks the same: the tetrahedron, cube, octahedron, dodecahedron, and icosahedron. 3/n
As a companion to our recent paper on "Repulsive Curves," we're releasing a dataset of hundreds of *extremely* difficult knots: cs.cmu.edu/~kmcrane/Proje…
In each case, a knotted & canonical embedding is given. Can you recover the right knot? 1/5
We've already tried a few dozen methods—from classics like "KnotPlot", to baselines like L-BFGS, to bleeding-edge algorithms like AQP, BCQN, etc.
For many knots, most these methods get "stuck," and don't reach a nice embedding.
Even our method doesn't hit 100%—but is close! 2/5
But this is a pretty unique problem relative to "standard" energies in geometry processing
For one thing, it considers all O(n²) pairs of points rather than just neighbors in a mesh
For another, you can't just *avoid* collisions—you have to maximize your distance from them. 3/5
Uniform edge lengths are a reasonable proxy for mesh quality, since equilateral triangles have good angles. But if you have more specific criteria, you can usually do better. "Hodge-Optimized Triangulations" provides a nice discussion of this perspective: geometry.caltech.edu/pubs/MMdGD11.p…
A popular approach in graphics is something like Botsch & Kobbelt, "Remeshing for Multiresolution Modeling" (Sec. 4): graphics.rwth-aachen.de/media/papers/r…
Basic idea: iteratively move vertices to equalize areas, split long edges, collapse short edges, & flip edges to improve vertex valence.
In this class of algorithms, I really like Chen & Holst, "Efficient mesh optimization schemes based on Optimal Delaunay Triangulations": math.uci.edu/~chenlong/Pape…
Here there's a clear notion of optimality, & an efficient preconditioner. For surfaces: restrict to tangential motions