Comments (5)
This is the major body joints to quickly initialize the body orientation. The indices (3, 4, 9, 10) are 1-st based, thus -1 makes them 0-based. Actually with enough views this step can be safely ignored.
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@YinghaoHuang91 Thank you very much for the reply!
Then what is the Head_VID mean?
Line 29 in 199f51e
Does it mean the head top vertices ID of SMPL?
I saw in line 27 you map the LSP keypoint to SMPL's, and the headtop keypoint was not read into line SMPL_JOINTS_IDS,
Line 27 in 199f51e
how did you deal with the headtop keypoint?
I have some problem about how do map the openpose nose point to SMPL jaw.
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The HEAD_VID is used in Line 41 of file "smpl_batch.py". Yes it's the SMPL vertex index to act as the proxy for head 2D landmark. Since SMPL doesn't have facial landmarks, one way is to manually pick the close vertices. You can do the same thing for other 2D landmarks if you want to stick with SMPL. Or you can try other more recent version of SMPL, like SMPL-X or STAR.
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thanks for quick reply!
I got this, so where can I check these VIDs of SMPL vertices? e.g. I want to find the nose, eyes and ears for openpose corresponding.
I considered about using SMPL-X, but their works mainly used python3.6....
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I found the nose_vid in smplx repo's smpl-h vid doc, but I haven't test it.
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Related Issues (14)
- When I run this code, I get an error HOT 4
- About the test code HOT 3
- The dynamic model sequence is shaking HOT 1
- Can the code be run on CPU? HOT 1
- Resulting shape and pose is not good. HOT 4
- Would you mind providing the version of each required packages?
- Strange Pose fitting results when using OpenPose J2D input HOT 5
- I think There is missing in psbody file or something
- Can not see code about shape. HOT 5
- With a larger picture like 1024*1024, the train results poor loss like 10E6 HOT 4
- About the Use of Silhouette HOT 8
- The result shape is strange HOT 5
- About camera pose HOT 1
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