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LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals
Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibrati...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611544/ https://www.ncbi.nlm.nih.gov/pubmed/34354294 http://dx.doi.org/10.1038/s41592-021-01226-z |
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author | Gosztolai, Adam Günel, Semih Ríos, Victor Lobato Abrate, Marco Pietro Morales, Daniel Rhodin, Helge Fua, Pascal Ramdya, Pavan |
author_facet | Gosztolai, Adam Günel, Semih Ríos, Victor Lobato Abrate, Marco Pietro Morales, Daniel Rhodin, Helge Fua, Pascal Ramdya, Pavan |
author_sort | Gosztolai, Adam |
collection | PubMed |
description | Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D’s versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals. |
format | Online Article Text |
id | pubmed-7611544 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-76115442021-08-20 LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals Gosztolai, Adam Günel, Semih Ríos, Victor Lobato Abrate, Marco Pietro Morales, Daniel Rhodin, Helge Fua, Pascal Ramdya, Pavan Nat Methods Article Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D’s versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals. 2021-08-01 2021-08-05 /pmc/articles/PMC7611544/ /pubmed/34354294 http://dx.doi.org/10.1038/s41592-021-01226-z Text en https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms |
spellingShingle | Article Gosztolai, Adam Günel, Semih Ríos, Victor Lobato Abrate, Marco Pietro Morales, Daniel Rhodin, Helge Fua, Pascal Ramdya, Pavan LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title | LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title_full | LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title_fullStr | LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title_full_unstemmed | LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title_short | LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals |
title_sort | liftpose3d, a deep learning-based approach for transforming 2d to 3d pose in laboratory animals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611544/ https://www.ncbi.nlm.nih.gov/pubmed/34354294 http://dx.doi.org/10.1038/s41592-021-01226-z |
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