# Grounded Human-Object Interaction Hotspots from Video

## TL;DR

This paper introduces a weakly supervised method to learn human-object interaction hotspots directly from videos, enabling the prediction of how objects can be manipulated without extensive supervision.

## Contribution

It presents a novel approach that learns interaction hotspots from videos, reducing supervision needs and enabling anticipation of interactions for new object categories.

## Key findings

- Weakly supervised hotspots are competitive with strongly supervised methods.
- The approach can predict interaction hotspots for unseen object categories.
- Grounding affordances in real videos improves interaction understanding.

## 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.