mlsanityData to 7 Oct 2026

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

arXiv 2009.00564 ↗First posted 1 Sep 2020cs.CV cs.LG q-bio.NCcited by 18

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Abstract (arXiv)

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced predicting posture from videos directly, which quickly impacted neuroscience and biology more broadly. In this primer we review the budding field of motion capture with deep learning. In particular, we will discuss the principles of those novel algorithms, highlight their potential as well as pitfalls for experimentalists, and provide a glimpse into the future.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 3; 2024: 4; 2025: 1.

18 papers.

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  1. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
  2. 2022SuperAnimal pretrained pose estimation models for behavioral analysiscited by 38
  3. 2021Perspectives on individual animal identification from biology and computer visioncited by 31
  4. 2021End-to-End Trainable Multi-Instance Pose Estimation with Transformerscited by 30
  5. 2021AcinoSet: A 3D Pose Estimation Dataset and Baseline Models for Cheetahs in the Wildcited by 29
  6. 2021Transflower: probabilistic autoregressive dance generation with multimodal attentioncited by 22
  7. 2024Computer Vision for Primate Behavior Analysis in the Wildcited by 10
  8. 2021Measuring and modeling the motor system with machine learningcited by 7
  9. 2023Few-shot Geometry-Aware Keypoint Localizationcited by 6
  10. 2024Degrees of Freedom Matter: Inferring Dynamics from Point Trajectoriescited by 4
  11. 2023Automated Behavioral Analysis Using Instance Segmentationcited by 2
  12. 2022BlanketGen - A synthetic blanket occlusion augmentation pipeline for MoCap datasetscited by 1
6 more
  1. 2023Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Userscited by 1
  2. 2024Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Functioncited by 1
  3. 2022A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasetscited by 0
  4. 2022Machine Learning for Optical Motion Capture-driven Musculoskeletal Modelling from Inertial Motion Capture Datacited by 0
  5. 2024SuDA: Support-based Domain Adaptation for Sim2Real Motion Capture with Flexible Sensorscited by 0
  6. 2025TrackStudio: An Integrated Toolkit for Markerless Trackingcited by 0