I’m constantly attempting new setups in rigging. Usually this is out of necessity, such as when current scripted setups don’t behave reliably or deliver the desired result with new models. Other attempts are me exploring things about my setups with which I’m not one hundred percent satisfied. I’ll keep doing research until I find a setup that works as I feel it should in the majority of situations, stably and predictably.

Stretchy Spline IK is something I’ve never liked because of the way its chains overshoot the end of the curve when the spline’s curvature is too great. I’ve never worked in a studio that had its own custom Spline IK solver, where this problem is non-existent, so I research solutions whenever I hit the issues that stretchy splines always bring.

Recently I had a thought about using live sub-curves of a spline (using the Maya subCurve node). I’ve always figured that the overshoots are due to floating point rounding and the fact that measuring a nurbs curve is an inexact science; curves are sub-samples a few thousand times, and the distances between points are summed.

(Before you ask, I use splines because they’re easy and light when you need to be able to lock a chain to a minimum or maximum length, but still allow stretching. I have not found a fast way of doing the same using a nurbs ribbon.)

I wrote a custom node to extract sub-curves by length along the original curve (using MFnNurbsCurve::findParamFromLength), then attached joints to each subCurve with Spline IK. I figured that sub-curves made by length would lock each joint in place, and that somehow this would work around the overshoot issue.

Boy was I wrong.

Turns out the assumption I’ve had for a while– the same assumption I’ve heard from other riggers– was itself incorrect. It’s not that the lengths aren’t being measured accurately enough, but that as the spline curves in on itself, the effective lengths of bones on it should shrink because the bones can’t bend to match. Obvious in hindsight, but it caught me by surprise. It’s like Manhattan Distance: in Euclidean space you may only be two kilometers from that pizzeria you love, but you end up walking three kilometers to get there because Euclidean distance doesn’t take into account the fact that we can’t pass through buildings. (Or: that sewers are winding and not always as easy to traverse as the city streets for your average turtle.)

On the up side I can think of a few good uses for the sub-curve node I made, so the experiment wasn’t a total loss. I also have a few ideas of how to use the curvature of the driving spline to come up with a scale value, meaning I have new experiments to carry out.

The test proved to me again that it never hurts to pull apart an established method in an attempt to do it better. “White belt mind,” a teacher of mine used to say– try to never lose that initial state we all have when we begin something new and are constantly trying to learn.