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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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A hardware prototype performs gaze estimation by optically encoding task-relevant features with a microlens array and mask, captured on a 4x4 phototransistor array and decoded by a small neural network, reaching 3.4 ms latency with competitive accuracy.
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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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Low Latency Gaze Tracking via Latent Optical Sensing
A hardware prototype performs gaze estimation by optically encoding task-relevant features with a microlens array and mask, captured on a 4x4 phototransistor array and decoded by a small neural network, reaching 3.4 ms latency with competitive accuracy.