ArticleInternational journal of computer vision2023
Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.
Article in International journal of computer vision, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Realistic monkey body animation reveals an uncanny valley in macaque body perception.PLoS biology · 2026Article
- Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.eNeuro · 2025Article
- Mapping the landscape of social behavior.Cell · 2025Article
- Mapping the landscape of social behavior.bioRxiv : the preprint server for biology · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.Nature methods · 2024Article
- Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools.bioRxiv : the preprint server for biology · 2024Article
- Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL.Nature communications · 2023Article
- Open-source software to calculate the static sciatic index automatically.Regenerative medicineArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance. Here, we introduce a semi-supervised learning strategy that incorporates unlabeled video frames via a simple temporal constraint applied during training. In freely moving mice, our new approach improves the current state-of-the-art performance of multi-view volumetric 3D pose estimation and further enhances the temporal stability and skeletal consistency of 3D tracking.
Identifiers
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Registered trials
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.