mlsanityData to 7 Oct 2026

LiveHPS++: Robust and Coherent Motion Capture in Dynamic Free Environment

arXiv 2407.09833 ↗First posted 13 Jul 2024cs.CVcited by 6

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Abstract (arXiv)

LiDAR-based human motion capture has garnered significant interest in recent years for its practicability in large-scale and unconstrained environments. However, most methods rely on cleanly segmented human point clouds as input, the accuracy and smoothness of their motion results are compromised when faced with noisy data, rendering them unsuitable for practical applications. To address these limitations and enhance the robustness and precision of motion capture with noise interference, we introduce LiveHPS++, an innovative and effective solution based on a single LiDAR system. Benefiting from three meticulously designed modules, our method can learn dynamic and kinematic features from human movements, and further enable the precise capture of coherent human motions in open settings, making it highly applicable to real-world scenarios. Through extensive experiments, LiveHPS++ has proven to significantly surpass existing state-of-the-art methods across various datasets, establishing a new benchmark in the field.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 58 references that are in the map. First posted on arXiv 13 Jul 2024.

Builds on

37 of 58 references.

References · 37

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25 more
  1. 2017Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUscited by 108
  2. 2021Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimationcited by 101
  3. 2018DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensorcited by 95
  4. 2018LiveCap: Real-time Human Performance Capture from Monocular Videocited by 93
  5. 2017Towards Accurate Markerless Human Shape and Pose Estimation over Timecited by 92
  6. 2020SSN: Shape Signature Networks for Multi-class Object Detection from Point Cloudscited by 74
  7. 2019EventCap: Monocular 3D Capture of High-Speed Human Motions using an Event Cameracited by 65
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  11. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  12. 2017Harvesting Multiple Views for Marker-less 3D Human Pose Annotationscited by 48
  13. 2023SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environmentscited by 47
  14. 2022LiDAR-aid Inertial Poser: Large-scale Human Motion Capture by Sparse Inertial and LiDAR Sensorscited by 43
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  18. 2021ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal Referencescited by 26
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  21. 2024LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environmentcited by 20
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 3.

6 papers.

  1. 2024Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D Human Pose Estimationcited by 4
  2. 2024Towards Practical Human Motion Prediction with LiDAR Point Cloudscited by 2
  3. 20253D Human Pose and Shape Estimation from LiDAR Point Clouds: A Reviewcited by 0
  4. 2026Sparkle: A Robust and Versatile Representation for Point Cloud based Human Motion Capturecited by 0
  5. 2026B'ezier Degradation Modeling for LiDAR-based Human Motion Capturecited by 0
  6. 2026Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integrationcited by 0