arXiv AI
Aug 11
Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
arXiv:2608. 09594v1 Announce Type: cross Abstract: Recently, AI-driven video generation has attracted considerable attention.
Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization.
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Hugging Face Trending Papers
Aug 19
CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation
CamWorldQA introduces the first benchmark for assessing the perceptual quality of camera‑controlled world video generation, featuring 720 videos generated by six methods from 20 source videos across six camera trajectories, each scored by human raters. The paper also presents CWQA, a no‑reference quality assessment network that combines spatial, temporal motion, and optical flow features to predict quality scores. Experiments show CWQA outperforms existing VQA methods on the CamWorldQA dataset.
arXiv AI
4d ago
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...
arXiv Computer Vision
Aug 24
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
arXiv:2608.20770v1 Announce Type: new Abstract: Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does...
Hugging Face Trending Papers
Jul 6
InFlux++: Real and Synthetic Data for Estimating Dynamic Camera Intrinsics
Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos.
arXiv AI
Aug 21
Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
arXiv:2608. 19556v1 Announce Type: cross Abstract: 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.
arXiv Computer Vision
Aug 31
Manifold4D: Denoising on Point Cloud Rendered Manifolds for Video Re-shooting
Manifold4D introduces a new denoising strategy for video re‑shooting that injects a rendered point‑cloud directly into the initial noise manifold, eliminating the need for the render to be an explicit conditioning stream during denoising. This approach allows the network to rely solely on the source video as a visual condition, improving camera‑control accuracy on the DAVIS‑Traj benchmark and Vista4D set, with significant reductions in rotation and translation errors while maintaining video fidelity. User studies confirm enhanced trajectory following and dynamic consistency, especially for large yaw amplitudes and even when the render is corrupted.
arXiv AI
2d ago
Video Generation Models: A Survey of Post-Training and Alignment
arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamic...
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.
arXiv AI
4d ago
Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
arXiv AI
3d ago
ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing
arXiv:2609.40356v1 Announce Type: cross Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits th...
arXiv Computer Vision
2d ago
4Director: Controlling Video World Models with Rigid 3D Geometry
arXiv:2610.02160v1 Announce Type: new Abstract: Precise control over camera and object motion is essential for professional video production. Existing methods control objects only coarsely, through i...