Comments (1)
Hi, As in your case,My model is great at gt but worse at cpn,Adding some module can make gt better but cpn gets worse.What does the author think about this situation? ls there any way to solve it. Looking forward to your reply! Thanks!
Hi, thank you for the interest! Recent methods usually integrate 2D pose detection together with 3D pose reconstruction to achieve good performance. Based on our experiments, I do not think achieving good results on CPN is a proper problem setup. This issue has been discussed in the paper.
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Related Issues (11)
- I try to reproduce your code,but it seems that your file named utils.py lacks a function called "wrap" HOT 6
- ABOUT 3DHP HOT 4
- Can I know to change T value to 81? HOT 2
- Testing MPI_INF_3DHP dataset with refined model. HOT 1
- gradient computation HOT 2
- There is no causal_model in the common folder
- What joints did you use for training and testing on humaneva dataset (T=27, MRCNN) ?
- Subtract min Z-value?
- Global loss and shared individually connected layers HOT 4
- About the results of gt 21.0
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