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Found 10 papers, 5 papers with code
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions.
Video Instance Matting
To remedy this deficiency, we propose Video Instance Matting~(VIM), that is, estimating alpha mattes of each instance at each frame of a video sequence.
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets.
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking
Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance in crowded scenes or in wide spaces.
Making Higher Order MOT Scalable: An Efficient Approximate Solver for Lifted Disjoint Paths
We present an efficient approximate message passing solver for the lifted disjoint paths problem (LDP), a natural but NP-hard model for multiple object tracking (MOT).
Lifted Disjoint Paths with Application in Multiple Object Tracking
We present an extension to the disjoint paths problem in which additional \emph{lifted} edges are introduced to provide path connectivity priors.
Ranked #2 on
Multi-Object Tracking
on 2D MOT 2015
Multiple People Tracking using Body and Joint Detections
We evaluate our framework on the MOT16/17 benchmark.
Ranked #32 on
Multi-Object Tracking
on MOT17
(MOTA metric)
Recovering Accurate 3D Human Pose in The Wild Using IMUs and a Moving Camera
In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild.
Fusion of Head and Full-Body Detectors for Multi-Object Tracking
In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach.
Ranked #21 on
Multi-Object Tracking
on MOT16
Tracking with multi-level features
We present a novel formulation of the multiple object tracking problem which integrates low and mid-level features.
