# Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

arXiv: https://arxiv.org/abs/2003.03972 · HTML: https://mlsanity.com/paper/2003.03972/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 30 of 36)

- [EgoHumans: An Egocentric 3D Multi-Human Benchmark](https://mlsanity.com/paper/2305.16487/) (2023)
- [EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors](https://mlsanity.com/paper/2305.01599/) (2023)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking](https://mlsanity.com/paper/2106.06856/) (2021)
- [Harmony4D: A Video Dataset for In-The-Wild Close Human Interactions](https://mlsanity.com/paper/2410.20294/) (2024)
- [Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras](https://mlsanity.com/paper/2110.10355/) (2021)
- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [Real-Time Multi-View 3D Human Pose Estimation using Semantic Feedback to Smart Edge Sensors](https://mlsanity.com/paper/2106.14729/) (2021)
- [Graph Neural Networks for Cross-Camera Data Association](https://mlsanity.com/paper/2201.06311/) (2022)
- [Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking](https://mlsanity.com/paper/2106.11589/) (2021)
- [MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision](https://mlsanity.com/paper/2108.04869/) (2021)
- [BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos](https://mlsanity.com/paper/2212.07401/) (2022)
- [EgoHDM: An Online Egocentric-Inertial Human Motion Capture, Localization, and Dense Mapping System](https://mlsanity.com/paper/2409.00343/) (2024)
- [MV-SSM: Multi-View State Space Modeling for 3D Human Pose Estimation](https://mlsanity.com/paper/2509.00649/) (2025)
- [Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs](https://mlsanity.com/paper/2102.04091/) (2021)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [What if Agents Could Imagine? Reinforcing Open-Vocabulary HOI Comprehension through Generation](https://mlsanity.com/paper/2602.11499/) (2026)
- [A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion](https://mlsanity.com/paper/2207.07381/) (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)
- [3D Semantic Scene Perception using Distributed Smart Edge Sensors](https://mlsanity.com/paper/2205.01460/) (2022)
- [A Unified Multi-view Multi-person Tracking Framework](https://mlsanity.com/paper/2302.03820/) (2023)
- [MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark](https://mlsanity.com/paper/2403.20225/) (2024)
- [A survey of top-down approaches for human pose estimation](https://mlsanity.com/paper/2202.02656/) (2022)
- [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)
- [Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics](https://mlsanity.com/paper/2401.05412/) (2023)
- [RapidPoseTriangulation: Multi-view Multi-person Whole-body Human Pose Triangulation in a Millisecond](https://mlsanity.com/paper/2503.21692/) (2025)
- [IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools](https://mlsanity.com/paper/2605.20682/) (2026)

## Abstract (arXiv)

Estimating 3D poses of multiple humans in real-time is a classic but still challenging task in computer vision. Its major difficulty lies in the ambiguity in cross-view association of 2D poses and the huge state space when there are multiple people in multiple views. In this paper, we present a novel solution for multi-human 3D pose estimation from multiple calibrated camera views. It takes 2D poses in different camera coordinates as inputs and aims for the accurate 3D poses in the global coordinate. Unlike previous methods that associate 2D poses among all pairs of views from scratch at every frame, we exploit the temporal consistency in videos to match the 2D inputs with 3D poses directly in 3-space. More specifically, we propose to retain the 3D pose for each person and update them iteratively via the cross-view multi-human tracking. This novel formulation improves both accuracy and efficiency, as we demonstrated on widely-used public datasets. To further verify the scalability of our method, we propose a new large-scale multi-human dataset with 12 to 28 camera views. Without bells and whistles, our solution achieves 154 FPS on 12 cameras and 34 FPS on 28 cameras, indicating its ability to handle large-scale real-world applications. The proposed dataset is released at https://github.com/longcw/crossview_3d_pose_tracking.