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Thanks for your great work!
I noticed that you use different Mean and Std when training and evaluation. It seems that t2m_mean.py and t2m_std.npy are the mean and std of the output of MDM.
I am confused that how did you get them? Do they have a significant impact on the final results? If I make improvements on MDM, does it mean that I need to obtain the t2m_mean.py and t2m_std.npy of my own model?
Looking forward to your reply, thank you again.
The text was updated successfully, but these errors were encountered:
Hi @yw0208 , thanks! Indeed the evaluators were trained with a slightly different normalization (See https://github.com/EricGuo5513/text-to-motion). I don't think it makes a dramatic difference in performance but please double check me.
Thanks for your great work!
I noticed that you use different Mean and Std when training and evaluation. It seems that t2m_mean.py and t2m_std.npy are the mean and std of the output of MDM.
I am confused that how did you get them? Do they have a significant impact on the final results? If I make improvements on MDM, does it mean that I need to obtain the t2m_mean.py and t2m_std.npy of my own model?
Looking forward to your reply, thank you again.
The text was updated successfully, but these errors were encountered: