mlsanityData to 7 Oct 2026

Learning to Estimate 3D Human Pose from Point Cloud

arXiv 2212.12910 ↗First posted 25 Dec 2022cs.CV cs.MMcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 25 Dec 2022.

Builds on

11 of 25 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  6. 2015Human Pose Estimation with Iterative Error Feedbackcited by 121
  7. 2017Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approachcited by 114
  8. 2017V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Mapcited by 102
  9. 2018Multi-Scale Structure-Aware Network for Human Pose Estimationcited by 37
  10. 2017Towards Good Practices for Deep 3D Hand Pose Estimationcited by 11
  11. 2018DRPose3D: Depth Ranking in 3D Human Pose Estimationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 1; 2026: 1.

9 papers.

  1. 20233D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labelscited by 9
  2. 2023PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Cloudscited by 9
  3. 2024HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Featurescited by 3
  4. 2024SPiKE: 3D Human Pose from Point Cloud Sequencescited by 2
  5. 2023VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Datacited by 1
  6. 2024Learning Localization of Body and Finger Animation Skeleton Joints on Three-Dimensional Models of Human Bodiescited by 1
  7. 2025ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restorationcited by 1
  8. 2023HumanReg: Self-supervised Non-rigid Registration of Human Point Cloudcited by 0
  9. 2026A Machine Learning Framework for Real-Time Personalized Ergonomic Pose Analysiscited by 0