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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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative 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.
SET is a new CUDA runtime framework that combines event-chaining, work-stealing, and per-stream buffers in graph-based pipelines to deliver 1.15-1.44X speedups and 18-54% lower scheduling overhead versus prior CUDA graph methods.
A literature survey that organizes neural 3D mesh texturing methods into a taxonomy spanning early GAN-based approaches to modern diffusion pipelines, while reviewing architectures, datasets, evaluation, and open challenges.
citing papers explorer
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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.
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SET: Stream-Event-Triggered Scheduling for Efficient CUDA Graph Pipelines
SET is a new CUDA runtime framework that combines event-chaining, work-stealing, and per-stream buffers in graph-based pipelines to deliver 1.15-1.44X speedups and 18-54% lower scheduling overhead versus prior CUDA graph methods.
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Advances in Neural 3D Mesh Texturing: A Survey
A literature survey that organizes neural 3D mesh texturing methods into a taxonomy spanning early GAN-based approaches to modern diffusion pipelines, while reviewing architectures, datasets, evaluation, and open challenges.