Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.09833 v1 pith:IVMW2R77 submitted 2024-07-13 cs.CV

classification cs.CV
keywords capturehumanmotionlivehpscoherentdynamicmethodsaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Sen-Cap estimates 3D human pose and global trajectory from flexible, uncalibrated combinations of LiDARs and cameras, setting state-of-the-art scores on Human-M3 and FreeMotion while tolerating point-cloud noise.

Pith tools