arXiv Computer Vision
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
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TRACE: Trajectory Representation and Consistency Estimation for AI-Generated Video Detection
arXiv:2609.25775v1 Announce Type: new Abstract: Recent advances in generative video models have enabled the synthesis of visually realistic content, posing significant challenges to synthetic video d...
arXiv Computer Vision
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MotionSpec: Spectral Trajectory Supervision for Motion-Consistent Video Generation
MotionSpec introduces a motion supervision framework for text-to-video generation that focuses on Spectral Trajectory Consistency (STC). STC builds dense anchor-relative motion trajectories, transforms them into spectral volumes, and aligns their amplitude and phase with target trajectories to constrain motion strength and temporal organization. The framework also adds Local Flow Consistency (LFC) to stabilize local motion transitions, resulting in improved motion consistency, temporal coherence, and plausibility while maintaining visual fidelity.
arXiv Machine Learning
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Physics-Driven Spatiotemporal Modeling for AI-Generated Video Detection
arXiv:2510. 08073v2 Announce Type: replace-cross Abstract: AI-generated videos have achieved near-perfect visual realism (e.
arXiv Computer Vision
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VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
arXiv:2510.24904v2 Announce Type: replace Abstract: Although recent video generative models are getting more capable of following external camera controls, imposed by either text descriptions or came...
arXiv AI
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MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos
arXiv:2609.37030v1 Announce Type: cross Abstract: Despite rapid progress in video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. Howev...
Hugging Face Trending Papers
Aug 20
Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
Streaming autoregressive diffusion models enable real-time, long-horizon video generation, but their training objectives optimize local frame prediction rather than the geometry and dynamics of a coherent world: long rollouts accumulate geometric drift and degrade into static or unnatural motion. Recent bidirectional approaches address this problem using rewards signals built upon 3D Gaussian-Splatting reconstruction.