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Found 14 papers, 5 papers with code
Harnessing Large Language Models for Training-free Video Anomaly Detection
Video anomaly detection (VAD) aims to temporally locate abnormal events in a video.
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation.
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis
Since video content is highly redundant, we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity, visual quality and impairs scalability.
Ranked #1 on
Text-to-Video Generation
on MSR-VTT
Delving into CLIP latent space for Video Anomaly Recognition
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision.
Interactive Neural Painting
This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP.
Quantum Multi-Model Fitting
Geometric model fitting is a challenging but fundamental computer vision problem.
Promptable Game Models: Text-Guided Game Simulation via Masked Diffusion Models
Most captivatingly, our PGM unlocks the director's mode, where the game is played by specifying goals for the agents in the form of a prompt.
Unsupervised Volumetric Animation
We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects.
InfiniCity: Infinite-Scale City Synthesis
Toward infinite-scale 3D city synthesis, we propose a novel framework, InfiniCity, which constructs and renders an unconstrainedly large and 3D-grounded environment from random noises.
Quantum Motion Segmentation
Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.
Playable Environments: Video Manipulation in Space and Time
We present Playable Environments - a new representation for interactive video generation and manipulation in space and time.
Playable Video Generation
This paper introduces the unsupervised learning problem of playable video generation (PVG).
Learning to Cluster under Domain Shift
While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i. e. labeled source data must be available.
