# Grounded Human-Object Interaction Hotspots from Video (Extended Abstract)

## TL;DR

This paper introduces a weakly supervised method to learn human-object interaction hotspots directly from video data, enabling the prediction of object manipulation points without extensive supervision, and generalizing to new object categories.

## Contribution

It presents a novel approach that learns interaction hotspots from videos, reducing supervision needs and improving generalization to unseen objects.

## Key findings

- Hotspot predictions are competitive with fully supervised methods.
- The approach generalizes to novel object categories.
- Grounded hotspots improve anticipation of object interactions.

## Abstract

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 anticipating 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 affordances in real human-object interactions. Not only are our weakly supervised hotspots competitive with strongly supervised affordance methods, but they can also anticipate object interaction for novel object categories. Project page: http://vision.cs.utexas.edu/projects/interaction-hotspots/