StippleDiffusion is a late-stage denoising ControlNet on an optimal-transport point-set diffusion baseline that produces capacity-constrained stipples from arbitrary density maps, generalizes to unseen point budgets, and matches optimization baselines on Icons-50 while remaining end-to-end trainable
Proceedings of the 2nd International Symposium on Non-Photorealistic Animation and Rendering , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
A semantics-driven optimization of URBS curves via score distillation sampling produces single continuous line drawings from text prompts or images.
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StippleDiffusion: Capacity-Constrained Stippling using Controlled Diffusion
StippleDiffusion is a late-stage denoising ControlNet on an optimal-transport point-set diffusion baseline that produces capacity-constrained stipples from arbitrary density maps, generalizes to unseen point budgets, and matches optimization baselines on Icons-50 while remaining end-to-end trainable
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Single-Line Drawing Generation via Semantics-Driven Optimization
A semantics-driven optimization of URBS curves via score distillation sampling produces single continuous line drawings from text prompts or images.