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garyzhao / SemGCN

The Pytorch implementation for "Semantic Graph Convolutional Networks for 3D Human Pose Regression" (CVPR 2019).
https://arxiv.org/abs/1904.03345
Apache License 2.0
465 stars 78 forks source link

Mapping COCO keypoints to Human36 format #27

Open aitikgupta opened 4 years ago

aitikgupta commented 4 years ago

I understand that Stacked Hourglass predictions (MPII format) are permuted to Human36 format. To infer, as mentioned here in issue #15, it is possible to pre-process annotations in-the-wild to Human36 ground-truth 2D format.

I was wondering if the same permuting work has been done for COCO keypoints format? Any pointers are appreciated. Thanks!

garyzhao commented 4 years ago

Hi @aitikgupta ,

Thanks for your interest in our work!

One possible solution for mapping COCO to H36M format can be found here: https://github.com/JimmySuen/integral-human-pose/blob/master/pytorch_projects/common_pytorch/dataset/hm36.py#L73

Or I think you can reduce all the numbers of key points to 13 and then mapping them.

Best, Long

HDYYZDN commented 3 years ago

@garyzhao ，您好， 请您推荐一个复现Stacked Hourglass Networks比较好的代码 ，非常期待您的回复

garyzhao commented 3 years ago

@garyzhao ，您好， 请您推荐一个复现Stacked Hourglass Networks比较好的代码 ，非常期待您的回复

Hi @HDYYZDN ,

You can check this one: https://github.com/bearpaw/pytorch-pose

Best, Long