An Improved Mixture Density Network for 3D Human Pose Estimation with Ordinal Ranking

Estimating accurate 3D human poses from 2D images remains a challenge due to the lack of explicit depth information in 2D data. This paper proposes an improved mixture density network for 3D human pose estimation called the Locally Connected Mixture Density Network (LCMDN). Instead of conducting dir...

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Detalles Bibliográficos
Autores principales: Wu, Yiqi, Ma, Shichao, Zhang, Dejun, Huang, Weilun, Chen, Yilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269848/
https://www.ncbi.nlm.nih.gov/pubmed/35808480
http://dx.doi.org/10.3390/s22134987

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269848/
https://www.ncbi.nlm.nih.gov/pubmed/35808480
http://dx.doi.org/10.3390/s22134987

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