mlsanityData to 14 Oct 2026

The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation

arXiv 2110.05132 ↗First posted 11 Oct 2021cs.CVcited by 18

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Abstract (arXiv)

We introduce CenterGroup, an attention-based framework to estimate human poses from a set of identity-agnostic keypoints and person center predictions in an image. Our approach uses a transformer to obtain context-aware embeddings for all detected keypoints and centers and then applies multi-head attention to directly group joints into their corresponding person centers. While most bottom-up methods rely on non-learnable clustering at inference, CenterGroup uses a fully differentiable attention mechanism that we train end-to-end together with our keypoint detector. As a result, our method obtains state-of-the-art performance with up to 2.5x faster inference time than competing bottom-up methods. Our code is available at https://github.com/dvl-tum/center-group .

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 55 of its 71 references that are in the map. First posted on arXiv 11 Oct 2021.

Builds on

55 of 71 references.

References · 55

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38 more
  1. 2019Objects as Pointscited by 1,175
  2. 2017Mixed Precision Trainingcited by 1,156
  3. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  4. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 915
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  16. 2018CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmarkcited by 214
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  18. 2017PoseTrack: A Benchmark for Human Pose Estimation and Trackingcited by 173
  19. 2019Distribution-Aware Coordinate Representation for Human Pose Estimationcited by 165
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  22. 2017Towards Accurate Multi-person Pose Estimation in the Wildcited by 131
  23. 2019PifPaf: Composite Fields for Human Pose Estimationcited by 120
  24. 2015DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimationcited by 117
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  29. 2017Multi-Context Attention for Human Pose Estimationcited by 66
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50 most cited of 55 All 55 (JSON)

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Cited by year

Citing papers per year: 2021: 1; 2022: 8; 2023: 4; 2024: 4; 2025: 1.

18 papers.

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  1. 2022DETRs with Hybrid Matchingcited by 150
  2. 2022JRDB-Pose: A Large-scale Dataset for Multi-Person Pose Estimation and Trackingcited by 25
  3. 2021Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimationcited by 20
  4. 2022QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Querycited by 13
  5. 2023Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguitycited by 13
  6. 2022Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimationcited by 10
  7. 2024Video-Based Human Pose Regression via Decoupled Space-Time Aggregationcited by 10
  8. 2023Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotationcited by 7
  9. 2022NIMBLE: A Non-rigid Hand Model with Bones and Musclescited by 6
  10. 2022AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regressioncited by 6
  11. 2022Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Personscited by 5
  12. 2023LAMP: Leveraging Language Prompts for Multi-person Pose Estimationcited by 4
6 more
  1. 2024Human Modelling and Pose Estimation Overviewcited by 4
  2. 2024DHRNet: A Dual-Path Hierarchical Relation Network for Multi-Person Pose Estimationcited by 2
  3. 2024GatedUniPose: A Novel Approach for Pose Estimation Combining UniRepLKNet and Gated Convolutioncited by 1
  4. 2022Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimationcited by 0
  5. 2023Hybrid model for Single-Stage Multi-Person Pose Estimationcited by 0
  6. 2025Keypoints as Dynamic Centroids for Unified Human Pose and Segmentationcited by 0