StippleDiffusion: Capacity-Constrained Stippling using Controlled Diffusion
Stipple patterns, point sets whose local density tracks a target image, are traditionally produced by per-density iterative optimizers, which are slow, non-differentiable, and must be re-run from scratch for each new target. Learned alternatives have so far addressed only unconditional point generation; capacity-constrained, image-conditioned stippling has remained out of reach. We present the first diffusion-based sampler that simultaneously satisfies a learned local point-distribution prior and a continuous, image-defined capacity constraint at inference. The method is a ControlNet branch built on top of an optimal-transport-grid point-set diffusion baseline, conditioned on the target d...
| Authors | Ofir Gilad, Aleksander Plocharski, Przemyslaw Musialski et al. |
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| Primary category | cs.GR |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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