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arXiv · 2412.18386

Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos

Sagnik Majumder·Tushar Nagarajan·Ziad Al-Halah·Kristen Grauman
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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 video settings for orchestrating which viewpoint should be displayed when, even when such settings come with limited labels. We demonstrate our idea on a variety of real-world videos from HowTo100M and Ego-Exo4D, and rigorously validate its advantages. Project: https://vision.cs.utexas.edu/projects/switch_a_view/.

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Sagnik Majumder, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman. 2024-12-24. Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos. https://arxiv.org/abs/2412.18386

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