Comments (3)
moberweger
commented on July 21, 2024
I am not sure, how they exactly used the DP++ for centre extraction. In general, the com refinement takes an approximate centre and predicts an update towards the true centre. The MCP of the middle finger is used as centre.
If you want to apply the com refinement on the FPHA benchmark, you should also train the com refinement on a similar dataset. The MSRA15 dataset is not well suited, since it has a third person viewpoint, whereas FPHA has a first person viewpoint.
So you should add another Dataset class, similar to MSRA15Dataset, and train the com refinement on this dataset. Then you can use this to refine the com, ie the location where the bounding volumen is cropped, before predicting the pose. Since the center-of-mass is rather inaccurate for egocentric viewpoints, I would suggest to use a temporal tracking of the com.
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chaiban
commented on July 21, 2024
Ok. Thank you for information. I will try that.
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Fazlik995
commented on July 21, 2024
Could u extract center points?
If so, could u help me with that issue?
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Related Issues (20)
- the code be written in tensorflow HOT 2
- Estimate 3D hand pose in ASL Finger Spelling Dataset HOT 1
- Is there guide to install DepthSense SDK HOT 1
- Deep prior for RGB HOT 1
- use ICVLImporter and NYUImporter HOT 6
- reference point(center point) HOT 3
- The network for refining hand localizatoin HOT 1
- This is some woring with the code! HOT 2
- I have a doubt about the hand detection algorithm. HOT 2
- Dataset issue HOT 1
- DHG-14/28 dataset center reference points
- About the joint refinement network? HOT 1
- Pretrained ICVL and NYU accuracy HOT 1
- about the openni2 and kinect HOT 1
- can it be trained without use icvl or nyu importer?
- can it be run without use icvl or nyu importer? HOT 5
- Could you provide Pretrain Model? HOT 1
- train/test split of the msra15 dataset HOT 5
- Could you provide more explanation about the input of the "RealtimeHandposePipeline" class HOT 1
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