# 3D Human Pose Estimation from a Single Image via Distance Matrix Regression

arXiv: https://arxiv.org/abs/1611.09010 · HTML: https://mlsanity.com/paper/1611.09010/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2016.

## Built on by (most cited first, 30 of 73)

- [Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows](https://mlsanity.com/paper/2107.13788/) (2021)
- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Neural Monocular 3D Human Motion Capture with Physical Awareness](https://mlsanity.com/paper/2105.01057/) (2021)
- [DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models](https://mlsanity.com/paper/2211.16487/) (2022)
- [HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery](https://mlsanity.com/paper/2304.05690/) (2023)
- [D&D: Learning Human Dynamics from Dynamic Camera](https://mlsanity.com/paper/2209.08790/) (2022)
- [Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction](https://mlsanity.com/paper/2303.13796/) (2023)
- [ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses](https://mlsanity.com/paper/2112.07088/) (2021)
- [Gravity-Aware Monocular 3D Human-Object Reconstruction](https://mlsanity.com/paper/2108.08844/) (2021)
- [HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance](https://mlsanity.com/paper/2205.05677/) (2022)
- [DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.02796/) (2022)
- [Egocentric Pose Estimation from Human Vision Span](https://mlsanity.com/paper/2104.05167/) (2021)
- [KAMA: 3D Keypoint Aware Body Mesh Articulation](https://mlsanity.com/paper/2104.13502/) (2021)
- [3D Human Pose, Shape and Texture from Low-Resolution Images and Videos](https://mlsanity.com/paper/2103.06498/) (2021)
- [Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation](https://mlsanity.com/paper/2203.15293/) (2022)
- [Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views](https://mlsanity.com/paper/2309.11962/) (2023)
- [Adaptive Multi-view and Temporal Fusing Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2110.05092/) (2021)
- [Recognizing Actions in Videos from Unseen Viewpoints](https://mlsanity.com/paper/2103.16516/) (2021)
- [Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation](https://mlsanity.com/paper/2107.13994/) (2021)
- [MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes](https://mlsanity.com/paper/2208.08439/) (2022)
- [HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE](https://mlsanity.com/paper/2204.01565/) (2022)
- [Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos](https://mlsanity.com/paper/2109.07353/) (2021)
- [Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking](https://mlsanity.com/paper/2106.11589/) (2021)
- [RAM: Recover Any 3D Human Motion in-the-Wild](https://mlsanity.com/paper/2603.19929/) (2026)
- [Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images](https://mlsanity.com/paper/2111.01884/) (2021)
- [A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation](https://mlsanity.com/paper/2311.03312/) (2023)
- [Everybody Is Unique: Towards Unbiased Human Mesh Recovery](https://mlsanity.com/paper/2107.06239/) (2021)
- [Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry](https://mlsanity.com/paper/2108.07777/) (2021)
- [VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data](https://mlsanity.com/paper/2207.09949/) (2022)
- [Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation](https://mlsanity.com/paper/2208.00090/) (2022)

## Abstract (arXiv)

This paper addresses the problem of 3D human pose estimation from a single image. We follow a standard two-step pipeline by first detecting the 2D position of the N body joints, and then using these observations to infer 3D pose. For the first step, we use a recent CNN-based detector. For the second step, most existing approaches perform 2N-to-3N regression of the Cartesian joint coordinates. We show that more precise pose estimates can be obtained by representing both the 2D and 3D human poses using N× N distance matrices, and formulating the problem as a 2D-to-3D distance matrix regression. For learning such a regressor we leverage on simple Neural Network architectures, which by construction, enforce positivity and symmetry of the predicted matrices. The approach has also the advantage to naturally handle missing observations and allowing to hypothesize the position of non-observed joints. Quantitative results on Humaneva and Human3.6M datasets demonstrate consistent performance gains over state-of-the-art. Qualitative evaluation on the images in-the-wild of the LSP dataset, using the regressor learned on Human3.6M, reveals very promising generalization results.