# Learning to Estimate 3D Human Pose from Point Cloud

arXiv: https://arxiv.org/abs/2212.12910 · HTML: https://mlsanity.com/paper/2212.12910/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 25 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Multi-Scale Structure-Aware Network for Human Pose Estimation](https://mlsanity.com/paper/1803.09894/) (2018)
- [Towards Good Practices for Deep 3D Hand Pose Estimation](https://mlsanity.com/paper/1707.07248/) (2017)
- [DRPose3D: Depth Ranking in 3D Human Pose Estimation](https://mlsanity.com/paper/1805.08973/) (2018)

## Built on by (most cited first, 9 of 9)

- [3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels](https://mlsanity.com/paper/2306.04745/) (2023)
- [PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds](https://mlsanity.com/paper/2308.14492/) (2023)
- [HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features](https://mlsanity.com/paper/2411.03086/) (2024)
- [SPiKE: 3D Human Pose from Point Cloud Sequences](https://mlsanity.com/paper/2409.01879/) (2024)
- [HumanReg: Self-supervised Non-rigid Registration of Human Point Cloud](https://mlsanity.com/paper/2312.05462/) (2023)
- [VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data](https://mlsanity.com/paper/2312.08871/) (2023)
- [Learning Localization of Body and Finger Animation Skeleton Joints on Three-Dimensional Models of Human Bodies](https://mlsanity.com/paper/2407.08484/) (2024)
- [ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration](https://mlsanity.com/paper/2503.06986/) (2025)
- [A Machine Learning Framework for Real-Time Personalized Ergonomic Pose Analysis](https://mlsanity.com/paper/2606.12988/) (2026)

## Abstract (arXiv)

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.