Thuman2.0 evaluation is coming out differently

[jingi0614]

Thank you for your great contribution to this paper.

I discovered that some data have a scale difference between the SMPLX normal map and the Thuman scan files during data preprocessing.
This seems to be affecting the evaluation. How should I resolve this?

Most of the data are correctly scaled, but there are some instances where the scales do not match.
I would appreciate any assistance you can provide. @River-Zhang

[jingi0614]

The issue was resolved after reprocessing the dataset using the new SMPL-X from THuman2.0. However, there is still a slight discrepancy in performance compared to the results reported in the SiFu paper. Could you help identify what might be causing this difference?

When visualizing the 4-directional feature maps used in SiFu, I've noticed a difference between the feature maps generated with the provided pretrained weights and those generated with the weights from the model I trained. I'm not sure why this discrepancy occurs, and I would greatly appreciate your assistance in understanding the possible reasons. @River-Zhang

This is the performance of the model I trained.
Thuman2.0
chamfer: tensor(0.7175)
p2s: tensor(0.7261)
NC: tensor(0.0449)
{'cape-easy-NC': 0.0338255874812603,
'cape-easy-chamfer': 0.7092598080635071,
'cape-easy-execution_time': 0.4802453056971232,
'cape-easy-p2s': 0.6766804456710815,
'cape-hard-NC': 0.0384397991001606,
'cape-hard-chamfer': 0.8673397302627563,
'cape-hard-execution_time': 0.4780437970161438,
'cape-hard-p2s': 0.7746955752372742}

[lywudi]

Thank you for your great contribution to this paper.谢谢您的伟大贡献，这一文件。

I discovered that some data have a scale difference between the SMPLX normal map and the Thuman scan files during data preprocessing.我发现，有些数据有一个规模之间的差SMPLX正常地图和Thuman扫描文件中的数据进行预处理。 This seems to be affecting the evaluation. How should I resolve this?这似乎是影响评估。 我应该怎么解决这个问题？ Most of the data are correctly scaled, but there are some instances where the scales do not match.大多数的数据是正确调整，但在某些情况下的尺度不相匹配。 I would appreciate any assistance you can provide. 我将感谢任何援助可以提供。@River-Zhang

Hello, I am training the model now, but I failed to process the human dataset, and I can't implement the processing process of ICON, can you share it