Comments (9)
@prismformore I'll upload my own implementation in a matter of days...
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@Lotayou
Here is my train dataset.
But, I don't think it is suitable for this work.
I should increase the number of images and take more various poses.
I will remove the link in a week.
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Thanks for the heads up. I'll keep on rolling with my work. Let's stay in touch and continue sharing info with each other. Good luck bro.
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Thanks!
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I've finished downloading the dataset. Feel free to disable the link anytime.
BTW, do you mind telling me how long does it take to train such a model (I have one Nvidia card with 12G memory)? I can use a rough timeline to coordinate with my colleagues.
Also, there's one thing puzzling me: It seems like pix2pixHD is designed to translate the segmentation masks to real images, not the skeleton stick-figures. Are we really supposed to use the skeleton as label instead of treating it as a normal RGB image from a different modality?
Anyway, I'll try using both body masks and image skeletons as input, gonna open a new repository on that too. I'll keep you notified if anything new happens.
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Hey, any progress? I've successfully trained pix2pix model with my own dataset and will publish the result these days
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@Lotayou I think the input of pix2pixHD is RGB image in this code
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@prismformore My thought exactly. using RGB input works well. It's just that nyoki's dataset convert skeleton to labels that I feel necessary to clarify this.
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@Lotayou Would u mind showing us your generated results with a comparison of different types of input (real image and skeleton)? Which one is better?
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Related Issues (14)
- 4.normalization.ipynb is null HOT 1
- where is network evaluate openpose_utils????? HOT 2
- pytube version
- There is no get_multplier in coco_eval.py HOT 1
- Synthesized Video Dataset
- project progress
- how do i start it
- where is the network package? from network.post import *
- Need I install open pose? HOT 6
- Making source images HOT 1
- Welcome to test my implementation
- when will hand support
- where is 'network/weight/pose_model.pth' HOT 1
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