What is the Input format of the ckpt_semgcn_nonlocal_sh.pth.tar model ? about semgcn HOT 4 CLOSED

Hi @duckduck-sys ,

It takes 2D input poses in H36M format.

You can use an original stacked hourglass model and convert its 2d predictions to H36M format by https://github.com/garyzhao/SemGCN/blob/master/data/prepare_data_2d_h36m_sh.py#L56

Best,
Long

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@garyzhao Thanks for the quick response!

So just to confirm, the correct inference process is as follows:

Step 0: Use a 2D pose estimation network such as Stacked Hourglass to generate a 2D pose in MPII format.
Step 1: Convert 2D pose from MPII format to H36M format using approach described here
Step 2: Pre-process the 2D input pose in some way.
Step 3: Use the pre-processed 2D pose in H36M format as input to the ckpt_semgcn_nonlocal_sh.pth.tar model.
Step 4: Output is a 3D pose in H36M format, visualize it.

Is my understanding correct? And is there any pre-processing involved in step 2, i.e. should the 2D pose be normalized in the pose bounding-box or?

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Hi @duckduck-sys ,

Yep, it's correct.

2D poses are scaled according to the image resolution and normalized to [-1, 1].

See https://github.com/garyzhao/SemGCN/blob/master/common/data_utils.py#L17

Best,
Long

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Thank you @garyzhao for the instructions. The 3D output i get looks weird, but i think it's related to the pre-processing, so I will raise the question in a new post.

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