mlsanityData to 7 Oct 2026

FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras

arXiv 1610.09534 ↗First posted 29 Oct 2016cs.CV cs.GR cs.ROcited by 23

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Abstract (arXiv)

Aiming at automatic, convenient and non-instrusive motion capture, this paper presents a new generation markerless motion capture technique, the FlyCap system, to capture surface motions of moving characters using multiple autonomous flying cameras (autonomous unmanned aerial vehicles(UAV) each integrated with an RGBD video camera). During data capture, three cooperative flying cameras automatically track and follow the moving target who performs large scale motions in a wide space. We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera. We leverage the using of visual-odometry information provided by the UAV platform, and formulate the surface tracking problem in a non-linear objective function that can be linearized and effectively minimized through a Gaussian-Newton method. Quantitative and qualitative experimental results demonstrate the competent and plausible surface and motion reconstruction results

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2016.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 5; 2024: 3; 2025: 0; 2026: 1.

23 papers.

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  1. 2023InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactionscited by 205
  2. 2022Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-timecited by 164
  3. 2021Editable Free-viewpoint Video Using a Layered Neural Representationcited by 148
  4. 2022A Survey of Non-Rigid 3D Registrationcited by 59
  5. 2023SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environmentscited by 47
  6. 2022NeuralHOFusion: Neural Volumetric Rendering under Human-object Interactionscited by 44
  7. 2022LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensorscited by 43
  8. 2021NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Camerascited by 40
  9. 2022HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDARcited by 38
  10. 2021Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactionscited by 37
  11. 2021ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal Referencescited by 26
  12. 2021TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensorscited by 24
11 more
  1. 2022HybridCap: Inertia-aid Monocular Capture of Challenging Human Motionscited by 19
  2. 2023Proactive Multi-Camera Collaboration For 3D Human Pose Estimationcited by 13
  3. 2022AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimationcited by 11
  4. 2023Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenescited by 11
  5. 2023Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recoverycited by 10
  6. 2024LiveHPS++: Robust and Coherent Motion Capture in Dynamic Free Environmentcited by 6
  7. 2024HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDARcited by 5
  8. 20213D Human Reconstruction in the Wild with Collaborative Aerial Camerascited by 4
  9. 2022Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Camerascited by 1
  10. 2024SPARE: Symmetrized Point-to-Plane Distance for Robust Non-Rigid 3D Registrationcited by 1
  11. 2026B'ezier Degradation Modeling for LiDAR-based Human Motion Capturecited by 0