Comments (3)
Dear authors,
Could you show a specific example of the preprocessing for the videos in the wild, e.g., how to generate accurate human mask and how to get the camera matrix? I saw in the paper, you mentioned that RVM is used for mask generation. However, the mask we obtained values between 0 and 1. Is a hard mask required for this task or we can just used the matting results?
Thanks
Hi, We need a hard mask, so you can use a threshold to filter the estimated results. And we get the camera matrix estimated by PARE. I will update some in-the-wild examples later, thanks for your feedback sincerely! :)
from monohuman.
Dear authors,
Could you show a specific example of the preprocessing for the videos in the wild, e.g., how to generate accurate human mask and how to get the camera matrix? I saw in the paper, you mentioned that RVM is used for mask generation. However, the mask we obtained values between 0 and 1. Is a hard mask required for this task or we can just used the matting results?
ThanksHi, We need a hard mask, so you can use a threshold to filter the estimated results. And we get the camera matrix estimated by PARE. I will update some in-the-wild examples later, thanks for your feedback sincerely! :)
@Yzmblog,
Hello,
I got the smpl values from PARE,
however, what about the camera intrinsic and extrinsic values in this case? Since PARE gives weak perspective cameras.
I found out 2 cases that the weak perspective cameras can be used as follows but which one is correct?
E = [[s, 0, 0, tx],
[0, s, 0, ty]
[ 0, 0, 1, 0],
[0, 0, 0, 1 ]]
another - [[ 1. 0. 0. tx]
[ 0. 1. 0. ty]
[ 0. 0. 1. s]
[ 0. 0. 0. 1. ]]
Could you provide information on how can I construct the cam params so I can process my file further for training
from monohuman.
Dear authors,
Could you show a specific example of the preprocessing for the videos in the wild, e.g., how to generate accurate human mask and how to get the camera matrix? I saw in the paper, you mentioned that RVM is used for mask generation. However, the mask we obtained values between 0 and 1. Is a hard mask required for this task or we can just used the matting results?
ThanksHi, We need a hard mask, so you can use a threshold to filter the estimated results. And we get the camera matrix estimated by PARE. I will update some in-the-wild examples later, thanks for your feedback sincerely! :)
@Yzmblog, Hello,
I got the smpl values from PARE, however, what about the camera intrinsic and extrinsic values in this case? Since PARE gives weak perspective cameras.
I found out 2 cases that the weak perspective cameras can be used as follows but which one is correct? E = [[s, 0, 0, tx], [0, s, 0, ty] [ 0, 0, 1, 0], [0, 0, 0, 1 ]]
another - [[ 1. 0. 0. tx] [ 0. 1. 0. ty] [ 0. 0. 1. s] [ 0. 0. 0. 1. ]]
Could you provide information on how can I construct the cam params so I can process my file further for training
Hi, you should first convert (s, tx, ty) to (tx, ty, tz), and then the second format is right.
from monohuman.
Related Issues (20)
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