mlsanityData to 7 Oct 2026

OmniPose: A Multi-Scale Framework for Multi-Person Pose Estimation

arXiv 2103.10180 ↗First posted 18 Mar 2021cs.CV cs.LG eess.IVcited by 6

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Abstract (arXiv)

We propose OmniPose, a single-pass, end-to-end trainable framework, that achieves state-of-the-art results for multi-person pose estimation. Using a novel waterfall module, the OmniPose architecture leverages multi-scale feature representations that increase the effectiveness of backbone feature extractors, without the need for post-processing. OmniPose incorporates contextual information across scales and joint localization with Gaussian heatmap modulation at the multi-scale feature extractor to estimate human pose with state-of-the-art accuracy. The multi-scale representations, obtained by the improved waterfall module in OmniPose, leverage the efficiency of progressive filtering in the cascade architecture, while maintaining multi-scale fields-of-view comparable to spatial pyramid configurations. Our results on multiple datasets demonstrate that OmniPose, with an improved HRNet backbone and waterfall module, is a robust and efficient architecture for multi-person pose estimation that achieves state-of-the-art results.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

32 of 41 references.

References · 32

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  12. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 573
20 more
  1. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
  2. 2017Cascaded Pyramid Network for Multi-Person Pose Estimationcited by 341
  3. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  4. 2016Convolutional Pose Machinescited by 325
  5. 2013DeepPose: Human Pose Estimation via Deep Neural Networkscited by 305
  6. 2017Integral Human Pose Regressioncited by 255
  7. 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
  8. 2019Distribution-Aware Coordinate Representation for Human Pose Estimationcited by 165
  9. 2018PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Modelcited by 133
  10. 2017Towards Accurate Multi-person Pose Estimation in the Wildcited by 131
  11. 2019Single-Stage Multi-Person Pose Machinescited by 95
  12. 2017Multi-Context Attention for Human Pose Estimationcited by 66
  13. 2019Rethinking on Multi-Stage Networks for Human Pose Estimationcited by 58
  14. 2020UniPose: Unified Human Pose Estimation in Single Images and Videoscited by 49
  15. 2017MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Priorcited by 36
  16. 2016Recurrent Human Pose Estimationcited by 27
  17. 2019Human Pose Estimation with Spatial Contextual Informationcited by 21
  18. 20183D Human Pose Estimation with 2D Marginal Heatmapscited by 20
  19. 2019Cascade Feature Aggregation for Human Pose Estimationcited by 8
  20. 2019Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentationcited by 5

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2.

6 papers.

  1. 2023LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmarkcited by 169
  2. 2023A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decisioncited by 11
  3. 2021BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representationscited by 3
  4. 2024Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approachcited by 2
  5. 2024Waterfall Transformer for Multi-person Pose Estimationcited by 1
  6. 2022SHOP: A Deep Learning Based Pipeline for near Real-Time Detection of Small Handheld Objects Present in Blurry Videocited by 0