Comments (3)
Solved!
it was because of the unit
/shapy/attributes/attributes/dataloader/demo.py
class DEMO_A2S():
def __init__(
self,
#betas_folder='../samples/shapy_fit/',
ds_gender='female',
model_gender='neutral',
model_type='smplx',
rating_folder='../samples/attributes/'
):
....
#self.db['height_gt'] = self.db['heights'].astype(np.float32) #before
self.db['height_gt'] = self.db['heights'].astype(np.float32) / 100 #after
self.db['chest'] = self.db['bust'].astype(np.float32) / 100
self.db['waist'] = self.db['waist'].astype(np.float32) / 100
self.db['hips'] = self.db['hips'].astype(np.float32) / 100
In ModelAgency dataset file, units are all "cm", so i think it was not matched.
I have another question.
There are no "weights" GT data in ModelAgency file, so i cannot execute "ahwcwh2s".
Is there any dataset that satisfies all conditions?
- linguistic attributes
- heights
- weights
- waist
- hips
- bust
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@jongunee
I'm trying to figure out how to run the shapy model on my own RGB images, without needing any additional files like keypoints or vertices.
I would greatly appreciate it if you could provide me an explanation of how to run the code with my RGB images only - to get the measurements of the body
Thank you very much!
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Oh sorry for late reply...
I completely forgot about it.
Unfortunately, �what I've done is inferred using the A2S function.
That means, i inferred 3d mesh using linguistic attributes like below.
"attributes": [
2.4285714285714284,
2.5,
3.2857142857142856,
2.0714285714285716,
4.285714285714286,
3.0,
3.5,
3.9285714285714284,
3.357142857142857,
2.857142857142857,
1.4285714285714286,
1.7857142857142858,
3.0,
1.6428571428571428,
4.142857142857143
]
to 3d mesh output.
So i didn't use RGB images in this case.
If i test your case, let you know.
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