A conditional diffusion model using proprioception and multi-contact touch produces metric-scale, physically consistent 3D object reconstructions under hand occlusion.
In: International conference on machine learning
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DE-CM reaches state-of-the-art one-step FID of 1.70 on ImageNet 256x256 by decomposing PF-ODE trajectories into three critical sub-trajectories and using flow matching plus N2N mapping for stability.
DepthPilot generates physically consistent and clinically interpretable colonoscopy videos by injecting depth priors into diffusion models through parameter-efficient fine-tuning and replacing linear denoising weights with adaptive splines.
citing papers explorer
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Physically Grounded 3D Generative Reconstruction under Hand Occlusion using Proprioception and Multi-Contact Touch
A conditional diffusion model using proprioception and multi-contact touch produces metric-scale, physically consistent 3D object reconstructions under hand occlusion.
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Dual-End Consistency Model
DE-CM reaches state-of-the-art one-step FID of 1.70 on ImageNet 256x256 by decomposing PF-ODE trajectories into three critical sub-trajectories and using flow matching plus N2N mapping for stability.
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DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation
DepthPilot generates physically consistent and clinically interpretable colonoscopy videos by injecting depth priors into diffusion models through parameter-efficient fine-tuning and replacing linear denoising weights with adaptive splines.