# Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations

arXiv: https://arxiv.org/abs/1803.08244 · HTML: https://mlsanity.com/paper/1803.08244/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 18 of 18)

- [DOVE: Learning Deformable 3D Objects by Watching Videos](https://mlsanity.com/paper/2107.10844/) (2021)
- [SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation](https://mlsanity.com/paper/2404.02041/) (2024)
- [Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry](https://mlsanity.com/paper/2108.07777/) (2021)
- [PAUL: Procrustean Autoencoder for Unsupervised Lifting](https://mlsanity.com/paper/2103.16773/) (2021)
- [Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective](https://mlsanity.com/paper/2204.04730/) (2022)
- [MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views](https://mlsanity.com/paper/2207.09086/) (2022)
- [LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation](https://mlsanity.com/paper/2309.07243/) (2023)
- [DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension](https://mlsanity.com/paper/2109.00033/) (2021)
- [PoseKernelLifter: Metric Lifting of 3D Human Pose using Sound](https://mlsanity.com/paper/2112.00216/) (2021)
- [SVMAC: Unsupervised 3D Human Pose Estimation from a Single Image with Single-view-multi-angle Consistency](https://mlsanity.com/paper/2106.05616/) (2021)
- [Survey and Perspective on Social Emotions in Robotics](https://mlsanity.com/paper/2105.09647/) (2021)
- [Deep unsupervised 3D human body reconstruction from a sparse set of landmarks](https://mlsanity.com/paper/2106.12282/) (2021)
- [High Fidelity 3D Reconstructions with Limited Physical Views](https://mlsanity.com/paper/2110.11599/) (2021)
- [3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training](https://mlsanity.com/paper/2106.04274/) (2021)
- [End-to-End Learning of Multi-category 3D Pose and Shape Estimation](https://mlsanity.com/paper/2112.10196/) (2021)
- [Visual-based Positioning and Pose Estimation](https://mlsanity.com/paper/2204.09232/) (2022)
- ["Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation](https://mlsanity.com/paper/2205.05980/) (2022)
- [Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation](https://mlsanity.com/paper/2209.00618/) (2022)

## Abstract (arXiv)

The task of three-dimensional (3D) human pose estimation from a single image can be divided into two parts: (1) Two-dimensional (2D) human joint detection from the image and (2) estimating a 3D pose from the 2D joints. Herein, we focus on the second part, i.e., a 3D pose estimation from 2D joint locations. The problem with existing methods is that they require either (1) a 3D pose dataset or (2) 2D joint locations in consecutive frames taken from a video sequence. We aim to solve these problems. For the first time, we propose a method that learns a 3D human pose without any 3D datasets. Our method can predict a 3D pose from 2D joint locations in a single image. Our system is based on the generative adversarial networks, and the networks are trained in an unsupervised manner. Our primary idea is that, if the network can predict a 3D human pose correctly, the 3D pose that is projected onto a 2D plane should not collapse even if it is rotated perpendicularly. We evaluated the performance of our method using Human3.6M and the MPII dataset and showed that our network can predict a 3D pose well even if the 3D dataset is not available during training.