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arXiv 2110.00988 ↗First posted 3 Oct 2021cs.CVcited by 8

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Abstract (arXiv)

We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation. We model all keypoints belonging to a human or an object -- the pose -- as a graph and leverage insights from community detection to quantify the independence of keypoints. We use a graph centrality measure to assign training weights to different parts of a pose. Our proposed measure quantifies how tightly a keypoint is connected to its neighborhood. Our experiments show that our method outperforms all previous methods for human pose estimation with fine-grained keypoint annotations on the face, the hands and the feet with a total of 133 keypoints. We also show that our method generalizes to car poses.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 40 references that are in the map. First posted on arXiv 3 Oct 2021.

Builds on

27 of 40 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2017Mask R-CNNcited by 7,259
  4. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  5. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  6. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  7. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  8. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
  9. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
  10. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  11. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
  12. 2016Convolutional Pose Machinescited by 325
15 more
  1. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  2. 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
  3. 2016Associative Embedding: End-to-End Learning for Joint Detection and Groupingcited by 211
  4. 2018Markerless tracking of user-defined features with deep learningcited by 203
  5. 2020Whole-Body Human Pose Estimation in the Wildcited by 178
  6. 2018PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Modelcited by 133
  7. 2019PifPaf: Composite Fields for Human Pose Estimationcited by 120
  8. 2015DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimationcited by 117
  9. 2018ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Drivingcited by 59
  10. 2021OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Associationcited by 55
  11. 2018MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Networkcited by 47
  12. 2019Convolutional Relational Machine for Group Activity Recognitioncited by 34
  13. 2020GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervisioncited by 28
  14. 2020Detecting 32 Pedestrian Attributes for Autonomous Vehiclescited by 10
  15. 2020Perceiving Humans: from Monocular 3D Localization to Social Distancingcited by 6

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Citing papers per year: 2022: 4; 2023: 1; 2024: 2; 2025: 1.

8 papers.