Comments (1)
Hi @kayakaracakaya
Thanks for your attention on our papers.
You mentioned that you want to transfer the pose of the source to target.
Do you want to preserve the identity of the source image or target image?
If you would like to generate the Fig. 5 or Fig.9 in the paper, you may check
visual_tools/show_smooth.py
generates Figure 5 of the paper.
visual_tools/show_smooth_structure.py
generates Figure 9 of the paper.
For DG-Net, we focus on the changing appearance of the foreground object, i.e., the cloth of the person.
So it is not optimal for changing the pose.
For changing pose, I recommend several papers as follows. You may refer to them. Hope them could help you.
[1] Wei L, Zhang S, Gao W, et al. Person transfer gan to bridge domain gap for person re-identification CVPR 2018
[2] https://github.com/charliememory/Pose-Guided-Person-Image-Generation
from dg-net.
Related Issues (20)
- Train fp16 interrupt HOT 3
- Change the data set to train HOT 3
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- when I run train.py, print there error message. Can you help to solve the issue? Thank you ! HOT 1
- 关于inception score HOT 1
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- IndexError:Dimension out of range(excepted to be in range of [-1,0],but got 1) HOT 4
- how does two different networks extract appearance and clothing separately?
- appearance code and structure code HOT 1
- online feeding and identity supervision HOT 3
- test_2label报错 yaml.load()缺少 loader HOT 2
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