# Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning

arXiv: https://arxiv.org/abs/2001.05097 · HTML: https://mlsanity.com/paper/2001.05097/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Online Knowledge Distillation for Efficient Pose Estimation](https://mlsanity.com/paper/2108.02092/) (2021)
- [Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers](https://mlsanity.com/paper/2210.06110/) (2022)
- [DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation](https://mlsanity.com/paper/2203.08713/) (2022)
- [SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2503.14097/) (2025)
- [On the Query Strategies for Efficient Online Active Distillation](https://mlsanity.com/paper/2309.01612/) (2023)
- [UV R-CNN: Stable and Efficient Dense Human Pose Estimation](https://mlsanity.com/paper/2211.02337/) (2022)
- [Fast-HaMeR: Boosting Hand Mesh Reconstruction using Knowledge Distillation](https://mlsanity.com/paper/2603.16444/) (2026)

## Abstract (arXiv)

We present MoVNect, a lightweight deep neural network to capture 3D human pose using a single RGB camera. To improve the overall performance of the model, we apply the teacher-student learning method based knowledge distillation to 3D human pose estimation. Real-time post-processing makes the CNN output yield temporally stable 3D skeletal information, which can be used in applications directly. We implement a 3D avatar application running on mobile in real-time to demonstrate that our network achieves both high accuracy and fast inference time. Extensive evaluations show the advantages of our lightweight model with the proposed training method over previous 3D pose estimation methods on the Human3.6M dataset and mobile devices.