# Learning to Estimate Pose by Watching Videos

## TL;DR

This paper introduces an unsupervised approach to human pose estimation from videos by leveraging motion cues and a basic detector, achieving results comparable to supervised methods without manual labeling.

## Contribution

The authors propose a novel unsupervised training method for dense human pose estimation using motion cues from videos, eliminating the need for manual annotations.

## Key findings

- Achieves pose estimation accuracy close to supervised methods.
- Outperforms baseline methods in action recognition tasks.
- Validates effectiveness on challenging datasets.

## Abstract

In this paper we propose a technique for obtaining coarse pose estimation of humans in an image that does not require any manual supervision. While a general unsupervised technique would fail to estimate human pose, we suggest that sufficient information about coarse pose can be obtained by observing human motion in multiple frames. Specifically, we consider obtaining surrogate supervision through videos as a means for obtaining motion based grouping cues. We supplement the method using a basic object detector that detects persons. With just these components we obtain a rough estimate of the human pose. With these samples for training, we train a fully convolutional neural network (FCNN)[20] to obtain accurate dense blob based pose estimation. We show that the results obtained are close to the ground-truth and to the results obtained using a fully supervised convolutional pose estimation method [31] as evaluated on a challenging dataset [15]. This is further validated by evaluating the obtained poses using a pose based action recognition method [5]. In this setting we outperform the results as obtained using the baseline method that uses a fully supervised pose estimation algorithm and is competitive with a new baseline created using convolutional pose estimation with full supervision.