# Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image

arXiv: https://arxiv.org/abs/1907.11346 · HTML: https://mlsanity.com/paper/1907.11346/

## Key facts

- Cited by 132 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 30 of 132)

- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [MotionBERT: A Unified Perspective on Learning Human Motion Representations](https://mlsanity.com/paper/2210.06551/) (2022)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (2021)
- [Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation](https://mlsanity.com/paper/2104.14639/) (2021)
- [Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation](https://mlsanity.com/paper/2106.05969/) (2021)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [Multi-Person Extreme Motion Prediction](https://mlsanity.com/paper/2105.08825/) (2021)
- [Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes](https://mlsanity.com/paper/2104.07300/) (2021)
- [Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration](https://mlsanity.com/paper/2103.02845/) (2021)
- [HOI-M3:Capture Multiple Humans and Objects Interaction within Contextual Environment](https://mlsanity.com/paper/2404.00299/) (2024)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [UmeTrack: Unified multi-view end-to-end hand tracking for VR](https://mlsanity.com/paper/2211.00099/) (2022)
- [Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats](https://mlsanity.com/paper/2212.14474/) (2022)
- [HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery](https://mlsanity.com/paper/2304.05690/) (2023)
- [Reconstructing 3D Human Pose by Watching Humans in the Mirror](https://mlsanity.com/paper/2104.00340/) (2021)
- [Body Meshes as Points](https://mlsanity.com/paper/2105.02467/) (2021)
- [Human Pose as Compositional Tokens](https://mlsanity.com/paper/2303.11638/) (2023)
- [D&D: Learning Human Dynamics from Dynamic Camera](https://mlsanity.com/paper/2209.08790/) (2022)
- [Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation](https://mlsanity.com/paper/2107.00434/) (2021)
- [Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images](https://mlsanity.com/paper/2109.05885/) (2021)
- [PACE: Human and Camera Motion Estimation from in-the-wild Videos](https://mlsanity.com/paper/2310.13768/) (2023)
- [KTPFormer: Kinematics and Trajectory Prior Knowledge-Enhanced Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2404.00658/) (2024)
- [PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers](https://mlsanity.com/paper/2303.09187/) (2023)
- [Gravity-Aware Monocular 3D Human-Object Reconstruction](https://mlsanity.com/paper/2108.08844/) (2021)
- [Embodied Scene-aware Human Pose Estimation](https://mlsanity.com/paper/2206.09106/) (2022)
- [PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos](https://mlsanity.com/paper/2103.09009/) (2021)
- [Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization](https://mlsanity.com/paper/2203.11471/) (2022)
- [PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios](https://mlsanity.com/paper/2404.19722/) (2024)

## Abstract (arXiv)

Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. The code is available in https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE , https://github.com/mks0601/3DMPPE_POSENET_RELEASE.