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MengHao666 / Hand-BMC-pytorch

PyTorch reimplementation of hand-biomechanical-constraints (ECCV2020)

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Question about results of using BMC Loss in supervised 3d hand pose estimation

xxxshark opened this issue · comments

commented

Hi, thanks for your work. But I have some problems about BMC Loss.
I want to add BMC Loss in my experiment which estimates 3d hand pose from a single gray image. But, it's hard to converge. When I add BMC Loss, my original 3d keypoints loss will rise, so I have to decrease BMC's loss_weight util 1e-9.
After training, I compare this experiment with BMC Loss and the baseline without BMC Loss. The baseline without BMC Loss performs better in MPJPE and it has lower BMC Loss in the same test dataset, which means BMC Loss has a negative effect in my experiment.
Is it normal I have this conclusion? Or what maybe wrong in my experiment?

commented

I think you should first check if my joint order in computation is consisitent with yours. And the constraint range of mine is from several dataset. Also, the bmc loss is a weak-supervised loss and is proved to be effective in such weak-supervised setting.

commented

Thanks for your reply.
I check again and the joint order is same with yours. The constraint range of mine is calculated from my train dataset. The code in constraint range recalculation and loss calculation is same.