ActivityForensics is the first large-scale benchmark for temporally localizing activity-level forgeries in videos, paired with a diffusion-based baseline called TADiff.
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Denoising Diffusion Implicit Models
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Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose reverse process can be much faster to sample from. We empirically demonstrate that DDIMs can produce high quality samples $10 \times$ to $50 \times$ faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, and can perform semantically meaningful image interpolation directly in the latent space.
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- abstract Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose revers
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citing papers explorer
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Information Filtering via Variational Regularization for Robot Manipulation
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Diffusion Policy Policy Optimization
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Gated Memory Policy
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Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
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Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
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Contact-Rich Robotic Assembly in Construction via Diffusion Policy Learning
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A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
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DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
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EL3DD: Extended Latent 3D Diffusion for Language Conditioned Multitask Manipulation
EL3DD extends latent 3D diffusion with language inputs and reference demonstrations to improve success rates on sequential manipulation tasks in the CALVIN dataset.
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