Comments (4)
Appreciate your question!
Resolution is not a fundamental constraint of CPM. We can use a higher resolution for sharper images.
For example, by using 512x512 UV maps, we can get higher resolution images, as shown in the following figure.
(From left to right: 256x256 UV map (as shown in the paper), 512x512 UV map, and reference image)
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Thanks for the quick reply. Is 512 UV map obtained by another PRNet model or some upsampling technique? I may want to try ever larger size :)
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For the 512x512 UV map, I used the same PRNet model (no need to retrain).
There's will be another face_ind_512.txt
, uv_kpt_ind_512.txt
files. (As your request, I'll upload it later).
But it worth noting about this method:
- [01] Basic (As shown in the paper):
Input -> UV (256) -> Color (256) | Pattern (256) -> Output Image (256) - [02] Higher resolution UV (As shown in previous figure):
Input -> UV (512) -> Color (256) | Pattern (256)-> Output Image (512)
(Upsampling used in Pattern Mask & Color transferred TsmC) - [3] "Wholesome" Solution:
Input -> UV (512) -> Color (512) | Pattern (512) -> Output Image (512)
I'm using [2] because that requires no re-train step (PRNet, Color, Pattern).
To be specific, I do use upsampling for intermediate output (Color Transferred (bicubic resize), Pattern Mask (nearest neighbor resize)). (See attached image)
(Getting higher resolution, based on [2])
You might wonder why didn't I use [3]? Isn't it the best solution?
To do [3], we'll need to re-train Color Branch & Pattern Branch.
It's feasible, but I haven't done the job due to: time-efficiency (it'll be super slow), dataset (most of the makeup datasets are 256x256 only), etc. ugh!
Hope this helps!
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Got it, thanks for explanation. The upsampling makes sense.
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Related Issues (20)
- a problem in colab HOT 3
- About the Makeup Transfer dataset. HOT 4
- Unsatisfied results tested on customized images HOT 2
- How to perform partial makeup transfer HOT 2
- how to render uv map to normal image? HOT 2
- Setup throwing error HOT 3
- the code is too bad! waste my time!!! HOT 1
- It work so bad!!!!!!!!! HOT 1
- ValueError: The passed save_path is not a valid checkpoint: ./PRNet/net-data/256_256_resfcn256_weight HOT 2
- Suggest to loosen the dependency on albumentations HOT 2
- Training on new dataset HOT 3
- I directly input the UV texture image, how should I modify the code? HOT 5
- AttributeError: module 'segmentation_models_pytorch' has no attribute 'utils' HOT 1
- About the MT dataset HOT 2
- No module named 'blend_modes
- MT dataset HOT 1
- image-invariant region mask HOT 1
- No module named 'segmentation_models_pytorch.unet'
- No module named 'blend_modes'
- The corresponding package cannot be installed in colab and locally, because of the mac device? HOT 1
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