CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
Structure and content-guided video synthesis with diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
FrameCache uses a Screen-Cache-Match strategy and Trajectory-Aware Autoregressive Generation to convert past frames into causal guidance for temporally coherent human animation videos.
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.
citing papers explorer
-
CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection
CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
-
Screen, Cache, and Match: A Training-Free Causality-Consistent Reference Frame Framework for Human Animation
FrameCache uses a Screen-Cache-Match strategy and Trajectory-Aware Autoregressive Generation to convert past frames into causal guidance for temporally coherent human animation videos.
-
ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.