Getting whole SMPL-X Parameters from Demo.py about shapy HOT 4 CLOSED

Hi,

we use them SMPL-X body model, but do not train hand pose and facial expression. This is why the output contains only global orientation, body pose, body shape, and translation. The 'missing' parameters (expression, hand, eye pose etc.) are set to mean pose (all zeros), so they're not part of the output.

The different formats for body_pose and global_rot are just conversions between rotation representations. I assume you're expecting axis angle rotation representations for global orientation and body pose. You can convert global_rot which is a rotation matrix (1,3,3) to smpl-x format which is in axis angle (1,3). raw_global_rot is a 6D rotation representation which we use for training (see this paper).

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Thank you for your reply. If I want to get the full parameters, what tools I can use, for example, smplify-x? I am actually using a rgb-d cameras thus I can still provide depth information.

Update
I have successfully used smplify-x to get the SMPL-X parameters. However, the speed is extremely low... Is there any faster solution to get this? BTW, I found the translation parameter got back from smplify-x is "camera_translation". Is it the same as the "transl" used in shapy and the example I proposed in my first question?

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Another question is related to the scale of shapy and smplify-x. In the follwing meshlab environment, I plot the mesh obtained from shapy (the leftmost), the mesh obtained from smplify-x (the rightmost), and the point cloud obtained from rgb-d image (the middle). As we can see, the mesh obtained from smplify-x lies in the origin. However, the mesh from shapy deviates a lot, in terms of z-axis. Moreover, both shapy and smplify-x deviate y-axis a little bit. How to solve this problem? Is it because shapy and smplify-x lacks of depth information?

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@felixshing hey have you found the answer why we have a mismatch between camera params of shapy and smplify-x ? I am also having the same issue.

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