4D Visualization of Dynamic Events From Unconstrained Multi-View Videos
Aayush Bansal, Minh Vo, Yaser Sheikh, Deva Ramanan, Srinivasa Narasimhan
Abstract
We present a data-driven approach for 4D space-time visualization of dynamic events from videos captured by hand-held multiple cameras. Key to our approach is the use of self-supervised neural networks specific to the scene to compose static and dynamic aspects of an event. Though captured from discrete viewpoints, this model enables us to move around the space-time of the event continuously. This model allows us to create virtual cameras that facilitate: (1) freezing the time and exploring views; (2) freezing a view and moving through time; and (3) simultaneously changing both time and view. We can also edit the videos and reveal occluded objects for a given view if it is visible in any of the other views. We validate our approach on challenging in-the-wild events captured using up to 15 mobile cameras.
BibTeX
@inproceedings{cvpr2020_4dvisualizationo,
title = {4D Visualization of Dynamic Events From Unconstrained Multi-View Videos},
author = {Aayush Bansal and Minh Vo and Yaser Sheikh and Deva Ramanan and Srinivasa Narasimhan},
booktitle = {CVPR 2020},
year = {2020}
}