A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.
Improved denoising diffusion probabilistic models
6 Pith papers cite this work. Polarity classification is still indexing.
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MultiAnimate adds Identifier Assigner and Identifier Adapter modules to diffusion video models so they can handle multiple characters without identity mix-ups, generalizing from two-character training data to more characters.
Reward models used as quality scorers in text-to-image generation encode demographic biases that cause reward-guided training to sexualize female subjects, reinforce stereotypes, and reduce diversity.
EGLOCE erases target concepts in diffusion models at inference time by optimizing latents with dual energy guidance that repels unwanted concepts while retaining prompt alignment.
Reward Forcing combines EMA-Sink tokens and Rewarded Distribution Matching Distillation to deliver state-of-the-art streaming video generation at 23.1 FPS without copying initial frames.
Two new lightweight modules for diffusion-based real-world image super-resolution deliver competitive perceptual quality and better structure preservation on DIV2K and RealSR datasets.
citing papers explorer
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From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion
A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.
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MultiAnimate: Pose-Guided Image Animation Made Extensible
MultiAnimate adds Identifier Assigner and Identifier Adapter modules to diffusion video models so they can handle multiple characters without identity mix-ups, generalizing from two-character training data to more characters.
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Bias at the End of the Score
Reward models used as quality scorers in text-to-image generation encode demographic biases that cause reward-guided training to sexualize female subjects, reinforce stereotypes, and reduce diversity.
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EGLOCE: Training-Free Energy-Guided Latent Optimization for Concept Erasure
EGLOCE erases target concepts in diffusion models at inference time by optimizing latents with dual energy guidance that repels unwanted concepts while retaining prompt alignment.
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Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
Reward Forcing combines EMA-Sink tokens and Rewarded Distribution Matching Distillation to deliver state-of-the-art streaming video generation at 23.1 FPS without copying initial frames.
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Degradation-Aware and Structure-Preserving Diffusion for Real-World Image Super-Resolution
Two new lightweight modules for diffusion-based real-world image super-resolution deliver competitive perceptual quality and better structure preservation on DIV2K and RealSR datasets.