arXiv Computer Vision
VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
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arXiv Computer Vision
Aug 21
OmniCamera: A Unified Framework for Multi-task Video Generation with Arbitrary Camera Control
arXiv:2604. 06010v2 Announce Type: replace Abstract: Video fundamentally intertwines two crucial axes: the dynamic content of a scene and the camera motion through which it is observed.
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...
arXiv Computer Vision
Sep 11
Dream4D: Lifting Camera-Controlled I2V towards Spatiotemporally Consistent 4D Generation
Dream4D is a new framework for generating spatiotemporally coherent 4D content. It uses a two‑stage pipeline: first, few‑shot learning predicts optimal camera trajectories from a single image; second, a pose‑conditioned diffusion process creates geometrically consistent multi‑view sequences that are converted into a persistent 4D representation. The method uniquely combines rich temporal priors from video diffusion models with geometric awareness from reconstruction models, achieving higher quality metrics such as mPSNR and mSSIM compared to existing approaches.
arXiv Computer Vision
Aug 25
BulletTime: Decoupled Control of Time and Camera Pose for Video Generation
BulletTime presents a 4D‑controllable video diffusion framework that separates scene dynamics from camera pose, allowing precise manipulation of both temporal and spatial aspects of generated videos. The model conditions on continuous world‑time sequences and camera trajectories, integrating them via a 4D positional encoding in the attention layer and adaptive normalizations for feature modulation. A specially curated dataset with independently parameterized temporal and camera variations is used for training, and the resulting system demonstrates robust real‑world 4D control while maintaining high generation quality and surpassing prior methods in controllability.
arXiv Computer Vision
1d ago
Did You Steal My Shot? Pioneering Camera Motion Plagiarism Detection in Generative Videos
arXiv:2609.22267v1 Announce Type: new Abstract: Camera motion often reflects directorial intent and requires professional equipment, making it a high value form of intellectual property. However, gen...
arXiv Computer Vision
Sep 2
CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling
CameraEditor is a new framework that transforms camera-controlled image editing into a temporal sequence prediction problem. By using video diffusion models, it incorporates a geometric perception module and dynamic reference routing to create precise visual references through dynamic panorama cropping. The method also inserts intermediate transition frames to handle large perspective shifts, maintaining content identity and spatial coherence, and is evaluated on a dataset of 5,760 instances with a benchmark of 462 test cases, achieving state‑of‑the‑art performance.