mlsanityData to 7 Oct 2026

3D Human Reconstruction in the Wild with Collaborative Aerial Cameras

arXiv 2108.03936 ↗First posted 9 Aug 2021cs.RO cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Aerial vehicles are revolutionizing applications that require capturing the 3D structure of dynamic targets in the wild, such as sports, medicine, and entertainment. The core challenges in developing a motion-capture system that operates in outdoors environments are: (1) 3D inference requires multiple simultaneous viewpoints of the target, (2) occlusion caused by obstacles is frequent when tracking moving targets, and (3) the camera and vehicle state estimation is noisy. We present a real-time aerial system for multi-camera control that can reconstruct human motions in natural environments without the use of special-purpose markers. We develop a multi-robot coordination scheme that maintains the optimal flight formation for target reconstruction quality amongst obstacles. We provide studies evaluating system performance in simulation, and validate real-world performance using two drones while a target performs activities such as jogging and playing soccer. Supplementary video: https://youtu.be/jxt91vx0cns

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 48 references that are in the map. First posted on arXiv 9 Aug 2021.

Builds on

22 of 48 references.

References · 22

  1. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  2. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  3. 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
  4. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
  5. 2018Pose Flow: Efficient Online Pose Trackingcited by 80
  6. 2016FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Camerascited by 23
  7. 2019Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environmentscited by 18
  8. 2019Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Makingcited by 16
  9. 2020AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learningcited by 11
  10. 2019Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematographycited by 10
  11. 2019Active Perception based Formation Control for Multiple Aerial Vehiclescited by 8
  12. 2018Human Motion Capture Using a Dronecited by 7
10 more
  1. 2018Multiview Supervision By Registrationcited by 7
  2. 2018Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filmingcited by 4
  3. 2019Optimizing for Aesthetically Pleasing Quadrotor Camera Motioncited by 4
  4. 2020Optimal Trajectory Planning for Cinematography with Multiple Unmanned Aerial Vehiclescited by 4
  5. 2020Do You See What I See? Coordinating Multiple Aerial Cameras for Robot Cinematographycited by 4
  6. 2019Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goalscited by 3
  7. 2020Autonomous Execution of Cinematographic Shots with Multiple Dronescited by 3
  8. 2020Active localization of multiple targets using noisy relative measurementscited by 2
  9. 2020Autonomous Planning for Multiple Aerial Cinematographerscited by 2
  10. 2020End-to-End 3D Multi-Object Tracking and Trajectory Forecastingcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023Proactive Multi-Camera Collaboration For 3D Human Pose Estimationcited by 13
  2. 2023Greedy Perspectives: Multi-Drone View Planning for Collaborative Perception in Cluttered Environmentscited by 2
  3. 2025PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Datacited by 1
  4. 2022Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAVcited by 0