Comments (4)
Hi,
Thanks for reading the code,
A) Yes, keeping the 3D aspect ratio is all this segment of code about.
B) Note that pts
is the GT 2D keypoint annotation. The goal of the last three line is to align the weakly-perspective projected 3D joints with the 2D annotation. I.e., if you plot pts_3d[:, :2]
to image, you will find they are very close to pts
.
C) I didn't understand the question. See the \hat{Y} equation at 5th page middle right column in the paper.
D) To keep z in range (0 ~ 256) as (x, y), actually it is not necessary for that it is re-normalized to -1 ~ 1 in https://github.com/xingyizhou/pytorch-pose-hg-3d/blob/master/src/datasets/h36m.py#L76, you can remove both lines for simplicity (I haven't tried).
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Hi,
Thanks for the prompt response.
I have to admit that I'm confused by lines 58 and 76 in h36m.py, and line 38 in FusionCriterion.py.
If x,y,z are the true coordinates, is the output of the network trained to be 2 * (x,y,z) / output resolution?
Why use this calibration choice?
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Hi,
A very detailed explanation of why/how to use this calibration can be found in Section 3.2 of https://arxiv.org/pdf/1803.09331.pdf .
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Hi Xingyi,
I think neither https://github.com/xingyizhou/pytorch-pose-hg-3d/blob/master/src/datasets/h36m.py#L76 nor https://github.com/xingyizhou/pytorch-pose-hg-3d/blob/master/src/datasets/h36m.py#L58 will keep z in a specific range like (0 ~ 256) or (-1 ~ 1). As an extreme example, we can think about two points that have the same x and y value while one of them is extremely far away and has a very big z value.
But it will do no harm to the final result since the ratio is kept all the way.
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Related Issues (20)
- s = min(s, max(img.shape[0], img.shape[1])) * 1.0 AttributeError: 'NoneType' object has no attribute 'shape' HOT 4
- Confidence of Detected Joints
- The defination in dataset file
- h36m benchmark result for categories
- Stage1: 2D pre-trained model trained to epoch=0?
- about the permission of pre-processed Human3.6M dataset
- about the pre-processed of Human3.6M
- RUN ERROR
- Performance On MPI-INF Dataset
- The preprocess of mpii is incorrect when input_h!=input_w
- The src point is wrong in image.py
- some problems occur running demo HOT 2
- the model behave worse when detect the squat
- download the models HOT 2
- 段错误,核心已转储 HOT 1
- how to using the camera of my own pc??
- root joint
- I solved 。。。
- Is it possible to input 2D coordinates obtained by other methods to obtain 3D coordinates in your model?
- demo has error HOT 1
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