Comments (6)
If you are interested in Human3.6M in particular, you can use the definition of joints in JOINT_REGRESSOR_H36M, which are specifically estimated to reflect the Human3.6M set of joints.
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Thank you! I will try this regressor.
Furthermore, I may utilize a new dataset, MPI_INF_3DHP, to train the model. This dataset has labeled the keypoints of human toes and endpoints of hands. It may need some tools to generate a new regressor. It there any good advice?
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For this kind of locations (i.e., landmarks on the surface instead of joints in the interior of the body) it should be pretty easy to establish correspondence by manually selecting a vertex of the mesh. For the SMPL topology, the correspondences for some of the toes and hand endpoints have already been defined here.
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Thank you very very much!
It seems that this file contains corresspondences of SMPL-H and SMPL-X. Are SMPL corresspondences the same as one of them?
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SMPL and SMPL+H share the same topology, so you can use the correspondences from SMPL+H.
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I will try it now!
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Related Issues (20)
- How to train model with human3.6m datasets HOT 1
- Preprocess of ground truth keypoints_3d on Human3.6M HOT 1
- question when I read the code HOT 1
- questions about "mesh_downsampling.npz" HOT 1
- Regarding fully connected baseline HOT 4
- Compute 'A', 'D', 'U' matrices HOT 6
- How to get 3d joints from demo.py and visualize it HOT 3
- About the SMPLParamRegressor
- Praise from a newbie HOT 1
- Why do you use different focal length for training and inference?
- Running ‘demo.py' can't get good results HOT 2
- The problem of camera parameter “sc” HOT 1
- Loading Resnet50 pretrained?
- Asking for the weight of losses
- how to retrain this model in a new dataset with the real SMPL model
- preprocess datasets of h36m.py
- run demo.py
- wrong mesh volume
- The additional files could not be obtained.
- Pretrained model HOT 1
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