4D Visualization of Dynamic Events From Unconstrained Multi-View Videos

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 disc...

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Bibliographic Details
Main Authors: Bansal, Aayush, Vo, Minh, Sheikh, Yaser, Ramanan, Deva, Narasimhan, Srinivasa
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computational modeling
Estimation
Image reconstruction
Three-dimensional displays
Videos
Visualization
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Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR42600.2020.00541