Seq2seq motion continuity and limb invariance constrained spatial-temporal encoder for 3D human pose estimation.

Fan Wei, Guanghua Xu, Qingqiang Wu, Penglin Qin, Peiyuan Tian, Leijun Pan, Yihua Zhao

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In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

7 authors.

Fan WeiSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Guanghua XuSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Qingqiang WuSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Penglin QinSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Peiyuan TianSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Leijun PanSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Yihua ZhaoSchool of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have seen deep neural network-based methods for 3D human pose estimation from 2D poses, but most directly predict key point positions without leveraging limb-joint correlations during human movement. In this article, we propose a limb-invariance-constrained hybrid spatiotemporal encoder, which extracts spatiotemporal and motion features of joints and limbs from continuous frames and uses limb invariance to boost key point estimation accuracy. Experiments on common datasets show it achieves state-of-the-art performance, supporting broader applications such as human-computer interaction and medical rehabilitation.

Indexed as

BiomechanicsMachine learning

Identifiers

PMID41438066

DOI10.1016/j.isci.2025.114094

PMCPMC12719767

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.