Presentation
Improving Curl Noise
DescriptionWe introduce a divergence-free nD vector noise defined as the n-dimensional cross product of the gradients of n-1 noise functions. We show that this vector noise function is divergence-free and hence volume preserving for any dimension n. Our method enables precise integration and extends to new settings by substituting noise functions with implicit surfaces, (hyper)surfaces, or custom functions. We demonstrate applications including image warping, surface texturing, noise bounded by implicit surfaces, anisotropic curl-noise, and high-dimensional point jittering up to 7D.
Event Type
Technical Papers
TimeTuesday, 16 December 20253:00pm - 3:11pm HKT
LocationMeeting Room S221, Level 2