Curvature Space Editing of Highly-Coiled Hair

Alvin Shi, Florence Bertails, A.M. Darke, and Theodore Kim

Code My Crown's Headwrap Curls model (top left) with our ruffling (bottom left) and Blender's scraggle modifiers (top right). Our ruffling method operates in curvature space, while Blender's noise-based scraggle operates in position space. The resultant texture from ruffling better resembles the right-hand side of the reference image from Curlsmith (bottom right).

Abstract

Due to its highly curved geometry, tightly coiled hair is challenging to model and edit using standard position-based tools. In this work we propose using material curvatures and twists to analyze and edit tightly coiled hair styles. Our method relies on the geometry of super-helices, primitives parametrized by piecewise constant curvatures and twists, whose helical geometry naturally resembles a coiled hair strand. Using this curvature/twist space, we introduce new editing tools that allow us to expand, shrink, "ruffle", interpolate or guide the position of coiled hair in a natural way. We present analytical expressions for geometry and gradients that allow our method to run efficiently and without the need for any training data. We successfully apply our tools to highly coiled simulated hairs, as well as those generated procedurally.

Supplement Video (YouTube) PDF(Compressed) Coding Resources

Raw Videos

Interpolation - Interpolating strand poses in various ways
Position Interpolation, Global Curvature Interpolation, Local Curvature Interpolation
Armatures - Animated armature demonstrations
Wisp I (Curly), Wisp II (Wavy)
Cage Comparison - Cage twisting versus armature twisting
Cage Twist, Armature Twist
Ponytails - Coiling a full head's ponytails
Before, After
Eye Covering - Moving a lock of coiled hair to cover the eye
Before, After
Stretching - Stretching a full head's hair to get different textures
Before, After
Ruffling - Demonstrations of our ruffling operator
Before, After, Parameter Interpolation, Noise-Based Scraggle

Acknowledgemenets

This work was supported by the Teng and Han Family Fund, the Bungie Foundation, and NSF IIS-2132280. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.