Comments (1)
Hi, thanks for your asking.
Yes, you are correct. We will loss some limb structure in this setting, thus I think it may affect the pose module, but this is the only thing can do.
That's why in my paper, I only use the G1 method on OneHand10K, instead of G1+6, as we know that G6 may be broken for many fingers. For example, if the middle point of one finger is invisible, the lime structure of this finger will be broken into two parts.
For the OneHand10K dataset, I just want to demonstrate my algorithm is also efficient on different dataset, as the this dataset is the few 2D hand pose datasets I can use. For a better understanding of it, it's better to ask the owner of it.
Besides, I think the invisible keypoint issue is a system error, which is introduced by the OneHand10K dataset. If this dataset itself does not care about invisible keypoints, I think there is no need to spend much time to fix this issue on it.
For your last question, "how can we get ....", it's really difficult to say, as this is the goal for all Computer Vision algorithm, a better and stabler method. It's better to refer to the latest CVPR/ICCV/ECCV papers to find better solution :).
Again, thanks for your kindly asking. Hops this can solve your concern.
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Related Issues (20)
- demo output HOT 10
- mediapipe hand landmark detect HOT 1
- loss function HOT 1
- How to use NSRM in HR-Net HOT 6
- How to get 21 keypoints annotations after cropping the original image? HOT 2
- hand tightest bounding box? HOT 1
- CMU Panoptic file HOT 5
- What do mask1, mask2 and mask3 mean๏ผ HOT 1
- predict two hands from single picture HOT 5
- The results of other test images is error HOT 1
- Encounter segfault when running inference.py HOT 2
- ./CPM used in training? HOT 1
- src.augmentation missing
- model structure in code incompatible with in paper HOT 5
- Required dataset HOT 1
- Question about LM loss HOT 1
- Can the model predict hand boundary boxes on its own? HOT 2
- Can you provide the OneHand 10k dataset? HOT 1
- How to run the OneHand10K dataset
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