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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 2
citation-polarity summary
fields
cs.CV 4years
2026 4roles
method 2polarities
use method 2representative citing papers
Reference-frame dominance in self-attention suppresses motion in image-to-video models; DyMoS rebalances attention from generated frames to the reference during initial denoising steps to improve dynamics while preserving fidelity.
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.
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
citing papers explorer
-
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.
-
Rebalancing Reference Frame Dominance to Improve Motion in Image-to-Video Models
Reference-frame dominance in self-attention suppresses motion in image-to-video models; DyMoS rebalances attention from generated frames to the reference during initial denoising steps to improve dynamics while preserving fidelity.
-
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.
-
Detecting AI-Generated Videos with Spiking Neural Networks
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.