# A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera

## TL;DR

This paper introduces a deep multitask framework that jointly estimates 3D human poses and recognizes actions from RGB videos, utilizing a two-stage process with real-time 2D detection, neural architecture search, and efficient modeling.

## Contribution

It proposes a novel integrated deep learning framework combining 2D pose detection, neural architecture search, and spatio-temporal modeling for joint 3D pose estimation and action recognition.

## Key findings

- Effective on Human3.6M, MSR Action3D, SBU Kinect datasets
- Requires low computational resources
- Achieves accurate 3D pose and action recognition

## Abstract

We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important keypoints of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, MSR Action3D and SBU Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.