Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos
Sagnik Majumder, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman
Abstract
We introduce Switch-a-View, a model that learns to automatically select the viewpoint to display at each timepoint when creating a how-to video. The key insight of our approach is how to train such a model from unlabeled--but human-edited--video samples. We pose a pretext task that pseudo-labels segments in the training videos for their primary viewpoint (egocentric or exocentric), and then discovers the patterns between the visual and spoken content in a how-to video on the one hand and its view-switch moments on the other hand. Armed with this predictor, our model can be applied to new multi-view videos to orchestrate which viewpoint should be displayed when. We demonstrate our idea on a variety of real-world videos from HowTo100M and Ego-Exo4D, and rigorously validate its advantages.
BibTeX
@InProceedings{Majumder_2025_ICCV,
author = {Majumder, Sagnik and Nagarajan, Tushar and Al-Halah, Ziad and Grauman, Kristen},
title = {Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11969-11979}
}