DUST decouples pose trajectories per camera source while sharing canonical Gaussians per agent to remove cross-source gradient conflicts and ghosting caused by temporal asynchrony in 4D cooperative driving scenes.
Raft: Recurrent all-pairs field transforms for optical flow
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DyMoS rebalances self-attention from generated frames to the reference frame in initial denoising steps of image-to-video models to reduce reference dominance and improve motion without training or fidelity loss.
LiBrA-Net achieves real-time native 4K video dehazing via Lie-algebraic bilateral affine fields and releases the first 4K paired dehazing video benchmark with per-frame annotations.
MAST with spiking neural networks achieves 93.14% mean accuracy detecting AI-generated videos from 10 unseen generators by exploiting smoother pixel residuals and compact semantic trajectories.
citing papers explorer
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One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstruction
DUST decouples pose trajectories per camera source while sharing canonical Gaussians per agent to remove cross-source gradient conflicts and ghosting caused by temporal asynchrony in 4D cooperative driving scenes.
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Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
DyMoS rebalances self-attention from generated frames to the reference frame in initial denoising steps of image-to-video models to reduce reference dominance and improve motion without training or fidelity loss.
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LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing
LiBrA-Net achieves real-time native 4K video dehazing via Lie-algebraic bilateral affine fields and releases the first 4K paired dehazing video benchmark with per-frame annotations.
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Detecting AI-Generated Videos with Spiking Neural Networks
MAST with spiking neural networks achieves 93.14% mean accuracy detecting AI-generated videos from 10 unseen generators by exploiting smoother pixel residuals and compact semantic trajectories.