# Ray3D: ray-based 3D human pose estimation for monocular absolute 3D localization

arXiv: https://arxiv.org/abs/2203.11471 · HTML: https://mlsanity.com/paper/2203.11471/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 55 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 30 of 38)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo](https://mlsanity.com/paper/2103.15595/) (2021)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [3D Human Pose Estimation with Spatial and Temporal Transformers](https://mlsanity.com/paper/2103.10455/) (2021)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [Unite the People: Closing the Loop Between 3D and 2D Human Representations](https://mlsanity.com/paper/1701.02468/) (2017)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [SPEC: Seeing People in the Wild with an Estimated Camera](https://mlsanity.com/paper/2110.00620/) (2021)
- [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1907.11346/) (2019)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach](https://mlsanity.com/paper/1704.02447/) (2017)
- [Exploiting temporal information for 3D pose estimation](https://mlsanity.com/paper/1711.08585/) (2017)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [Motion Guided 3D Pose Estimation from Videos](https://mlsanity.com/paper/2004.13985/) (2020)
- [Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image](https://mlsanity.com/paper/1701.00295/) (2017)
- [Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense](https://mlsanity.com/paper/1909.01507/) (2019)
- [SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation](https://mlsanity.com/paper/2008.11469/) (2020)
- [HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2008.00206/) (2020)
- [Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation](https://mlsanity.com/paper/2004.03143/) (2020)

## Built on by (most cited first, 25 of 25)

- [Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation](https://mlsanity.com/paper/2303.11579/) (2023)
- [GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video](https://mlsanity.com/paper/2307.05853/) (2023)
- [HMD-Poser: On-Device Real-time Human Motion Tracking from Scalable Sparse Observations](https://mlsanity.com/paper/2403.03561/) (2024)
- [Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation](https://mlsanity.com/paper/2307.03833/) (2023)
- [HSTFormer: Hierarchical Spatial-Temporal Transformers for 3D Human Pose Estimation](https://mlsanity.com/paper/2301.07322/) (2023)
- [CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators](https://mlsanity.com/paper/2407.06141/) (2024)
- [Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation](https://mlsanity.com/paper/2408.15810/) (2024)
- [Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment](https://mlsanity.com/paper/2408.08182/) (2024)
- [The Influence of Faulty Labels in Data Sets on Human Pose Estimation](https://mlsanity.com/paper/2409.03887/) (2024)
- [PersPose: 3D Human Pose Estimation with Perspective Encoding and Perspective Rotation](https://mlsanity.com/paper/2508.17239/) (2025)
- [Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach](https://mlsanity.com/paper/2304.00950/) (2023)
- [BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment](https://mlsanity.com/paper/2410.20731/) (2024)
- [CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty](https://mlsanity.com/paper/2412.10431/) (2024)
- [RUMPL: Ray-Based Transformers for Universal Multi-View 2D to 3D Human Pose Lifting](https://mlsanity.com/paper/2512.15488/) (2025)
- [FuRPE: Learning Full-body Reconstruction from Part Experts](https://mlsanity.com/paper/2212.00731/) (2022)
- [Spatio-temporal MLP-graph network for 3D human pose estimation](https://mlsanity.com/paper/2308.15313/) (2023)
- [Multi-hop graph transformer network for 3D human pose estimation](https://mlsanity.com/paper/2405.03055/) (2024)
- [LAMP: Localization Aware Multi-camera People Tracking in Metric 3D World](https://mlsanity.com/paper/2605.05390/) (2026)
- [EgoExoMoCap: Distributed Ego-Exo Human Motion Capture](https://mlsanity.com/paper/2607.15868/) (2026)
- [PoseGraphNet++: Enriching 3D Human Pose with Orientation Estimation](https://mlsanity.com/paper/2308.11440/) (2023)
- [Learning Human Motion from Monocular Videos via Cross-Modal Manifold Alignment](https://mlsanity.com/paper/2404.09499/) (2024)
- [Mocap-2-to-3: Multi-view Lifting for Monocular Motion Recovery with 2D Pretraining](https://mlsanity.com/paper/2503.03222/) (2025)
- [RayD3D: Distilling Depth Knowledge Along the Ray for Robust Multi-View 3D Object Detection](https://mlsanity.com/paper/2603.07493/) (2026)
- [UAV-OVO: Out-of-Viewpoint Generalization in UAV Action Recognition](https://mlsanity.com/paper/2605.25615/) (2026)
- [Field Converter: Geometry-Initialized Temporal Residual Refinement for World-Grounded Player Pose Estimation from Soccer Broadcasts](https://mlsanity.com/paper/2609.10498/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute human pose estimation with calibrated camera. Accurate and generalizable absolute 3D human pose estimation from monocular 2D pose input is an ill-posed problem. To address this challenge, we convert the input from pixel space to 3D normalized rays. This conversion makes our approach robust to camera intrinsic parameter changes. To deal with the in-the-wild camera extrinsic parameter variations, Ray3D explicitly takes the camera extrinsic parameters as an input and jointly models the distribution between the 3D pose rays and camera extrinsic parameters. This novel network design is the key to the outstanding generalizability of Ray3D approach. To have a comprehensive understanding of how the camera intrinsic and extrinsic parameter variations affect the accuracy of absolute 3D key-point localization, we conduct in-depth systematic experiments on three single person 3D benchmarks as well as one synthetic benchmark. These experiments demonstrate that our method significantly outperforms existing state-of-the-art models. Our code and the synthetic dataset are available at https://github.com/YxZhxn/Ray3D .