# Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

arXiv: https://arxiv.org/abs/2106.11589 · HTML: https://mlsanity.com/paper/2106.11589/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 23 of 23)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (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)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://mlsanity.com/paper/1711.07319/) (2017)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB](https://mlsanity.com/paper/1712.03453/) (2017)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1907.11346/) (2019)
- [DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model](https://mlsanity.com/paper/1605.03170/) (2016)
- [Cross View Fusion for 3D Human Pose Estimation](https://mlsanity.com/paper/1909.01203/) (2019)
- [Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views](https://mlsanity.com/paper/1901.04111/) (2019)
- [3D Human Pose Estimation from a Single Image via Distance Matrix Regression](https://mlsanity.com/paper/1611.09010/) (2016)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [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)
- [Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS](https://mlsanity.com/paper/2003.03972/) (2020)
- [Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation](https://mlsanity.com/paper/2007.02054/) (2020)
- [Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture](https://mlsanity.com/paper/1808.01525/) (2018)
- [Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views](https://mlsanity.com/paper/2101.09745/) (2021)

## Built on by (most cited first, 12 of 12)

- [DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation](https://mlsanity.com/paper/2203.08713/) (2022)
- [Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras](https://mlsanity.com/paper/2110.10355/) (2021)
- [Markerless Multi-view 3D Human Pose Estimation: a survey](https://mlsanity.com/paper/2407.03817/) (2024)
- [MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation](https://mlsanity.com/paper/2509.00649/) (2025)
- [A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion](https://mlsanity.com/paper/2207.07381/) (2022)
- [A survey of top-down approaches for human pose estimation](https://mlsanity.com/paper/2202.02656/) (2022)
- [VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2205.12602/) (2022)
- [VoxelKeypointFusion: Generalizable Multi-View Multi-Person Pose Estimation](https://mlsanity.com/paper/2410.18723/) (2024)
- [Simultaneously Recovering Multi-Person Meshes and Multi-View Cameras with Human Semantics](https://mlsanity.com/paper/2412.18785/) (2024)
- [Bottom-up approaches for multi-person pose estimation and it's applications: A brief review](https://mlsanity.com/paper/2112.11834/) (2021)
- [RapidPoseTriangulation: Multi-view Multi-person Whole-body Human Pose Triangulation in a Millisecond](https://mlsanity.com/paper/2503.21692/) (2025)
- [SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images](https://mlsanity.com/paper/2501.18478/) (2025)

## Abstract (arXiv)

This paper introduces an approach for multi-human 3D pose estimation and tracking based on calibrated multi-view. The main challenge lies in finding the cross-view and temporal correspondences correctly even when several human pose estimations are noisy. Compare to previous solutions that construct 3D poses from multiple views, our approach takes advantage of temporal consistency to match the 2D poses estimated with previously constructed 3D skeletons in every view. Therefore cross-view and temporal associations are accomplished simultaneously. Since the performance suffers from mistaken association and noisy predictions, we design two strategies for aiming better correspondences and 3D reconstruction. Specifically, we propose a part-aware measurement for 2D-3D association and a filter that can cope with 2D outliers during reconstruction. Our approach is efficient and effective comparing to state-of-the-art methods; it achieves competitive results on two benchmarks: 96.8% on Campus and 97.4% on Shelf. Moreover, we extends the length of Campus evaluation frames to be more challenging and our proposal also reach well-performed result.