Comments (9)
Hi, you can visit here
https://github.com/mks0601/3DMPPE_POSENET_RELEASE
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@mks0601
Thanks for your answer! I got it in this link .But a new problem arose that the followed picture when I used your vis.py to draw 3D pose. Your vis.py is here . I run demo.py to get 18 key points and then the code is as follows.
Could you tell me how to modify it to draw correctly?
Remark:I run demo.py and not test.py. Because I only test a random image.
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I only set a bounding box, so the number of person is 1.
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I'm not sure I got you. Could you clarify?
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Sorry,I didn't express myself clearly.
I have generated the 3D visual picture. 2D image and 3D image are as follows. It's obvious that the posture is incorrect in 3D space. What did I do wrong? How should I correct it?
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How did you get them? Did you run the demo code?
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Yes, I run the code and input my image, bbox_list and root depth which is from RootNet.
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I think you need to make tick of xyz have the same interval. Y-axis has an interval of 250, too high than 100 of x-axis and z-axis
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I got it. I set the range of all three axes to the example diagram you gave. The generated 3D image now looks normal. Thank you for your kind answer.
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Related Issues (20)
- Keypoints probability HOT 3
- 2.6Mdatasets
- Human3.6M
- Run environment and video tests.
- Run my own img on demo.py HOT 4
- something about bbox_list HOT 2
- something about dataset HOT 2
- Some extended problems
- about h36m parsing. HOT 1
- Problems with Reproduced Papers HOT 12
- Problem when visualize on MuPoTS using bbox_root provided by RootNet
- Change resnet type HOT 5
- Question regarding bbox and rootNet HOT 2
- Extracting human36 parsed images HOT 12
- About inference my own images HOT 4
- about human36 data File name format HOT 6
- other public datasets,overfitting issue HOT 6
- about datasets Human3.6m HOT 3
- Question about pretrained model used in demo. HOT 2
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