Why enlarge the bbox?

Hi, thank you for providing the code and such an insightful paper. I have a doubt, in line 63 you enlarge the bounding box by 25% in both directions. However, before that you sanitize the bounding box to ensure that the [xmin, ymin, xmax, ymax] are all within the image dimensions.
Is it possible that after enlarging our (xmin, ymin) or (xmax, ymax) might go outside image dimensions?

3DMPPE_ROOTNET_RELEASE/common/utils/pose_utils.py

Line 63 in 8bef0cd

bbox[2] = w*1.25

Secondly, I have a basic question: Are we preserving the aspect ratio as 1:1 of the bounding boxes, because the ratio of the human is assumed to be 2000mmx2000mm?

Hi,

For the first question, yes it can exceed the image size. But here and here can handle that issue.

For the second question, yes.

@mks0601 Thank you very much for your response. :)
Just as a small follow up, I notice that you mention in the ReadMe that we must use tight(not extended) bounding boxes during evaluation on different datasets. Then, why do we intentionally enlarge the bounding box in the above code?

Because the codes will automatically enlarge the box. If you fed enlarged box, the box will be enlarged two times.

True, thank you for the clarification.
Sorry to take the issue one step further, and may be it is a rookie question: what is the motivation behind enlarging the bounding box?
I could imagine, that may be the tight bounding boxes are so tight that the hair is not included or some clothing material (like hats haha or shoes) is not included within it. I don't know if I am thinking in right direction, but what is your motivation to enlarge the boxes?

This is a kind of convention in human pose estimation. I think it is to make sure the body part is not deleted after scale/rotation augmentation?

oh good to know. Yes, that seems more plausible reason than what I said! Thank you, closing the issue now! 👍