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una-dinosauria / human-motion-prediction

Simple baselines and RNNs for predicting human motion in tensorflow. Presented at CVPR 17.
MIT License
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Relationship about joint and dim_to_use? #56

Open tracer9 opened 5 years ago

tracer9 commented 5 years ago

Hi there, I am new to here and I am quite interested in this research topic. However, there is one question I cannot figure out. So I come here for help.

In your code, you throw away dimension whose std<1e-4. Say that there are 99 dims, of which 45 dims are useless, then 54 dims left, right?

As your said:

Regarding the 32 joints, I believe only 17 are independent, and the rest are end effectors as you call them.

However, when I print the useless dims index as here: [10 11 16 17 18 19 20 25 26 31 32 33 34 35 48 49 50 58 59 63 64 65 66 67 68 69 70 71 72 73 74 82 83 87 88 89 90 91 92 93 94 95 96 97 98]

I found that some of them are not correspond to one joint! For example, joint 3 should correspond dimension: 9, 10, 11. However, only dimension 10 and 11 here is dim-to-ignore.

This means, according to ignore these dimension, you simply violent the correspondance of the relationship: one joint ~ 3 dimension. Right?

The last quesion, although input dimension is 54, which DOES NOT REPRESENT 17 joints, the output dimenstion is also 54, which DOES REPRENT 17 joints, am i right?

This is a link from another issue.

Regarding the 32 joints, I believe only 17 are independent, and the rest are end effectors as you call them. IIRC some joints are repeated -- I remember observing this when I plotted the index in 3d as I was going down the tree, but you may want to confirm it yourself.

Originally posted by @una-dinosauria in https://github.com/una-dinosauria/human-motion-prediction/issues/23#issuecomment-365824784

lr_flip

Originally posted by @jutanke in https://github.com/una-dinosauria/human-motion-prediction/issues/46#

RuipengZhang118 commented 5 years ago

There is a unnormalization process behind the code that restores the dimension when the error is calculated.I think that.