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Found 11 papers, 2 papers with code
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence
In contrast to previous methods that rely on dense correspondences, we introduce the VideoSwap framework that exploits semantic point correspondences, inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape.
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks.
Unifying Tracking and Image-Video Object Detection
We propose TrIVD (Tracking and Image-Video Detection), the first framework that unifies image OD, video OD, and MOT within one end-to-end model.
Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation
Key findings are twofold: (1) by capturing the motion transfer with an ordinary differential equation (ODE), it helps to regularize the motion field, and (2) by utilizing the source image itself, we are able to inpaint occluded/missing regions arising from large motion changes.
Fast Adaptive Anomaly Detection
Anything outside of a given normal population is by definition an anomaly.
Joint Audio-Visual Deepfake Detection
Deepfakes ("deep learning" + "fake") are synthetically-generated videos from AI algorithms.
Ranked #6 on
DeepFake Detection
on FakeAVCeleb
Dance Dance Generation: Motion Transfer for Internet Videos
This work presents computational methods for transferring body movements from one person to another with videos collected in the wild.
Image2GIF: Generating Cinemagraphs using Recurrent Deep Q-Networks
Given a still photograph, one can imagine how dynamic objects might move against a static background.
Visual to Sound: Generating Natural Sound for Videos in the Wild
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world.
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future.
Temporal Perception and Prediction in Ego-Centric Video
Given a video of an activity, can we predict what will happen next?
