ArticleNature communications2023
Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
6 citing papers in PubMed, 24 citations in OpenAlex.
- Occlusion-Robust Cattle Pose Estimation for Precision Livestock Monitoring Using Hierarchical Locality Refinement.Animals : an open access journal from MDPI · 2026Article
- ViT-ConvGAN: a hybrid model for spatiotemporal action recognition using video transformer and 3D CNN.Scientific reports · 2026Article
- Mapping the landscape of social behavior.Cell · 2025Article
- A real-time, multi-subject three-dimensional pose tracking system for the behavioral analysis of non-human primates.Cell reports methods · 2025Article
- Mapping the landscape of social behavior.bioRxiv : the preprint server for biology · 2024Article
- Three-dimensional surface motion capture of multiple freely moving pigs using MAMMAL.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Understandings of the three-dimensional social behaviors of freely moving large-size mammals are valuable for both agriculture and life science, yet challenging due to occlusions in close interactions. Although existing animal pose estimation methods captured keypoint trajectories, they ignored deformable surfaces which contained geometric information essential for social interaction prediction and for dealing with the occlusions. In this study, we develop a Multi-Animal Mesh Model Alignment (MAMMAL) system based on an articulated surface mesh model. Our self-designed MAMMAL algorithms automatically enable us to align multi-view images into our mesh model and to capture 3D surface motions of multiple animals, which display better performance upon severe occlusions compared to traditional triangulation and allow complex social analysis. By utilizing MAMMAL, we are able to quantitatively analyze the locomotion, postures, animal-scene interactions, social interactions, as well as detailed tail motions of pigs. Furthermore, experiments on mouse and Beagle dogs demonstrate the generalizability of MAMMAL across different environments and mammal species.
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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.