Grounded Human-Object Interaction Hotspots from Video
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Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements. We propose an approach to learn human-object interaction "hotspots" directly from video. Rather than treat affordances as a manually supervised semantic segmentation task, our approach learns about interactions by watching videos of real human behavior and recognizing afforded actions. Given a novel image or video, our model infers a spatial hotspot map indicating how an object would be manipulated in a potential interaction -- even if the object is currently at rest. Through results with both first and third person video, we show the value of grounding affordance maps in real human-object interactions. Not only are our weakly supervised grounded hotspots competitive with strongly supervised affordance methods, but they can also anticipate object function for novel objects and enhance object recognition.
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