# MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

arXiv: https://arxiv.org/abs/1409.7963 · HTML: https://mlsanity.com/paper/1409.7963/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2014.

## Built on by (most cited first, 6 of 6)

- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey](https://mlsanity.com/paper/2301.06187/) (2023)
- [FootFormer: Estimating Stability from Visual Input](https://mlsanity.com/paper/2510.19170/) (2025)
- [Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes](https://mlsanity.com/paper/2310.08451/) (2023)

## Abstract (arXiv)

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.