Rethinking pose estimation in crowds: overcoming the detection information bottleneck and ambiguity

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Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity, ICCV 2023

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Authors: Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander

Affiliation: EPFL

Date: October, 2023

Here we provide neural networks weights for the best models in our article "Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity", ICCV 2023. Each model has the naming convention "dataset"-"modeltype".pth

These pth files can be loaded with PyTorch. The code to load and use the models is available at The code to load and use the models is available at: https://github.com/amathislab/BUCTD

Note: The weights for OCHuman, are called COCO-* as one only trains on COCO. So OCHuman-X := COCO-X

We also share the predictions from various bottom-up models to reproduce the training stored in *.json format (compressed as zip files). See our repository for more details.

Link to the ICCV article: 

https://openaccess.thecvf.com/content/ICCV2023/papers/Zhou_Rethinking_Pose_Estimation_in_Crowds_Overcoming_the_Detection_Information_Bottleneck_ICCV_2023_paper.pdf

The weights and predictions are released with Creative Commons Attribution 4.0 license. The code is released under the Apache 2.0 license, see https://github.com/amathislab/BUCTD 

If you find our weights, code or ideas useful, please cite:

@InProceedings{Zhou_2023_ICCV,

   author    = {Zhou, Mu and Stoffl, Lucas and Mathis, Mackenzie Weygandt and Mathis, Alexander},

   title     = {Rethinking Pose Estimation in Crowds: Overcoming the Detection Information Bottleneck and Ambiguity},

   booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

   month     = {October},

   year      = {2023},

   pages     = {14689-14699}

}