# Shape-aware Multi-Person Pose Estimation from Multi-View Images

arXiv: https://arxiv.org/abs/2110.02330 · HTML: https://mlsanity.com/paper/2110.02330/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 55 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 30 of 35)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation](https://mlsanity.com/paper/1908.10357/) (2019)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://mlsanity.com/paper/1611.05424/) (2016)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)
- [Learnable Triangulation of Human Pose](https://mlsanity.com/paper/1905.05754/) (2019)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model](https://mlsanity.com/paper/1803.08225/) (2018)
- [VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera](https://mlsanity.com/paper/1705.01583/) (2017)
- [DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation](https://mlsanity.com/paper/1511.06645/) (2015)
- [Towards Accurate Markerless Human Shape and Pose Estimation over Time](https://mlsanity.com/paper/1707.07548/) (2017)
- [Cross View Fusion for 3D Human Pose Estimation](https://mlsanity.com/paper/1909.01203/) (2019)
- [LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images](https://mlsanity.com/paper/1803.00455/) (2018)
- [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://mlsanity.com/paper/1901.04111/) (2019)
- [Human Body Model Fitting by Learned Gradient Descent](https://mlsanity.com/paper/2008.08474/) (2020)
- [Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation](https://mlsanity.com/paper/2004.02186/) (2020)
- [SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation](https://mlsanity.com/paper/2008.11469/) (2020)
- [4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras](https://mlsanity.com/paper/2002.12625/) (2020)
- [Harvesting Multiple Views for Marker-less 3D Human Pose Annotations](https://mlsanity.com/paper/1704.04793/) (2017)
- [HMOR: Hierarchical Multi-Person Ordinal Relations for Monocular Multi-Person 3D Pose Estimation](https://mlsanity.com/paper/2008.00206/) (2020)

## Built on by (most cited first, 24 of 24)

- [Capturing and Inferring Dense Full-Body Human-Scene Contact](https://mlsanity.com/paper/2206.09553/) (2022)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction](https://mlsanity.com/paper/2209.12354/) (2022)
- [Hi4D: 4D Instance Segmentation of Close Human Interaction](https://mlsanity.com/paper/2303.15380/) (2023)
- [PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence](https://mlsanity.com/paper/2203.01754/) (2022)
- [Learned Vertex Descent: A New Direction for 3D Human Model Fitting](https://mlsanity.com/paper/2205.06254/) (2022)
- [PACE: Human and Camera Motion Estimation from in-the-wild Videos](https://mlsanity.com/paper/2310.13768/) (2023)
- [TEMPO: Efficient Multi-View Pose Estimation, Tracking, and Forecasting](https://mlsanity.com/paper/2309.07910/) (2023)
- [Learning to Fit Morphable Models](https://mlsanity.com/paper/2111.14824/) (2021)
- [SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction](https://mlsanity.com/paper/2304.00359/) (2023)
- [WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation](https://mlsanity.com/paper/2501.02771/) (2025)
- [Improved Trajectory Reconstruction for Markerless Pose Estimation](https://mlsanity.com/paper/2303.02413/) (2023)
- [Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement](https://mlsanity.com/paper/2210.13529/) (2022)
- [Progressive Multi-view Human Mesh Recovery with Self-Supervision](https://mlsanity.com/paper/2212.05223/) (2022)
- [AvatarPose: Avatar-guided 3D Pose Estimation of Close Human Interaction from Sparse Multi-view Videos](https://mlsanity.com/paper/2408.02110/) (2024)
- [Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes](https://mlsanity.com/paper/2503.13739/) (2025)
- [MLPHand: Real Time Multi-View 3D Hand Mesh Reconstruction via MLP Modeling](https://mlsanity.com/paper/2406.16137/) (2024)
- [Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics](https://mlsanity.com/paper/2412.18785/) (2024)
- [Personalized 3D Human Pose and Shape Refinement](https://mlsanity.com/paper/2403.11634/) (2024)
- [Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization](https://mlsanity.com/paper/2512.05259/) (2025)
- [Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video](https://mlsanity.com/paper/2312.04784/) (2023)
- [C-DOG: Multi-View Multi-instance Feature Association Using Connected δ-Overlap Graphs](https://mlsanity.com/paper/2507.14095/) (2025)
- [Mesh-Aware Epipolar Matching for Multi-View Multi-Person 3D Pose Estimation in Basketball](https://mlsanity.com/paper/2605.29953/) (2026)
- [DynaTok: Token-Based 4D Reconstruction from Partial Point Clouds](https://mlsanity.com/paper/2606.12189/) (2026)

## Abstract (arXiv)

In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates noisy 2D observations from multiple camera views into 3D space and then associates them into individual instances based on a confidence-aware majority voting technique. The final pose estimates are attained from a novel optimization scheme which links high-confidence multi-view 2D observations and 3D joint candidates. Moreover, a statistical parametric body model such as SMPL is leveraged as a regularizing prior for these 3D joint candidates. Specifically, both 3D poses and SMPL parameters are optimized jointly in an alternating fashion. Here the parametric models help in correcting implausible 3D pose estimates and filling in missing joint detections while updated 3D poses in turn guide obtaining better SMPL estimations. By linking 2D and 3D observations, our method is both accurate and generalizes to different data sources because it better decouples the final 3D pose from the inter-person constellation and is more robust to noisy 2D detections. We systematically evaluate our method on public datasets and achieve state-of-the-art performance. The code and video will be available on the project page: https://ait.ethz.ch/projects/2021/multi-human-pose/.