# Generalized Feedback Loop for Joint Hand-Object Pose Estimation

## TL;DR

This paper introduces a feedback loop method using deep networks to improve 3D hand and object pose estimation from depth images, achieving real-time performance and outperforming existing methods in joint estimation tasks.

## Contribution

It presents a novel feedback loop framework with deep networks for joint hand-object 3D pose estimation, enhancing accuracy and efficiency.

## Key findings

- Performs on par with state-of-the-art for hand pose estimation.
- Outperforms existing methods for joint hand-object pose estimation.
- Operates in real-time on a single GPU.

## Abstract

We propose an approach to estimating the 3D pose of a hand, possibly handling an object, given a depth image. We show that we can correct the mistakes made by a Convolutional Neural Network trained to predict an estimate of the 3D pose by using a feedback loop. The components of this feedback loop are also Deep Networks, optimized using training data. This approach can be generalized to a hand interacting with an object. Therefore, we jointly estimate the 3D pose of the hand and the 3D pose of the object. Our approach performs en-par with state-of-the-art methods for 3D hand pose estimation, and outperforms state-of-the-art methods for joint hand-object pose estimation when using depth images only. Also, our approach is efficient as our implementation runs in real-time on a single GPU.