ha0tang / xinggan Goto Github PK

Hey @Ha0Tang
Thanks for sharing such awesome work!
I was wondering if we utilize your algorithm and facial landmarks to do face swapping and generate talking head models?

Thanks for this great work, you have mentioned the accuracy with both SA and AS blocks but under the absence of the co-attention fusion module in the paper and I wonder how did you get the result in this case? Did you have a direct FC layer at the end of the attention modules? How can we replicate that result?

@Ha0Tang Thanks for your novel work. I have trained the marker dataset follow your guidance. But I have a question that With the increase of training iteration, the loss of each part also increases except D_PP and D_PB。what are the two parts mean? I also wanted to ask how many epochs were used in the pre-training models you provided

Thanks for your novel work. When I calculated the SSIM metric, it's a little lower than the value that in the paper.
Here are the results that I obtained.
Market1501 dataset

Some visual results that obtained with the pretrained model.
/0265_c6s1_056351_01.jpg___0265_c3s1_062642_05.jpg_vis.jpg

1322_c3s3_035678_02.jpg___1322_c4s5_061435_01.jpg_vis.jpg

I don't know where I got it wrong.
Thanks for your time.

Can we run this code without GPU ?

Thanks for your novel work. It helps me a lot! And I wonder how to visualize the pose map as the figure shown in your paper? I can't find the visualization process in this repo.

Thanks for your excellent work. I want to conduct experiments on my own dataset. May I ask how to prepare my own dataset.

Under what license are you releasing this code?

Hi, why I always get [-1, -1] * 18 when I try to run compute_coordinates.py? Such as:
0000_c6s3_047142_03.jpg: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
0609_c5s2_022005_05.jpg: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
0304_c5s1_068698_01.jpg: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
-1_c6s4_006527_05.jpg: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]: [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
...
For any images I test, the cooridiantes always are [-1, -1] * 18, how can I solve this error?
Thanks a lot!

Dear paper authors

I am working on a NeurIPS paper for PoseMorphing, so I wanted a good comparison with your state-of-the-art method.

I tried to run your pretrained deepfashion model, and it worked. However, the results seem worse than I expected, can it be that the latest pytorch 1.7 has broken some detail in your version?

I write the command I used to run your model, and 2 example results. Maybe you can tell me if they look as expected, or something has gone wrong?

python test.py --dataroot deepfashion/ --name deepfashion_XingGAN --model XingGAN --phase test --dataset_mode keypoint --norm instance --batchSize 1 --resize_or_crop no --gpu_ids 0 --BP_input_nc 18 --no_flip --which_model_netG Xing --checkpoints_dir ./checkpoints --pairLst /deepfahion/fasion-resize-pairs-test.csv --which_epoch latest --results_dir ./results --display_id 0

fashionMENTees_Tanksid0000730104_7additional jpg___fashionMENTees_Tanksid0000730104_1front jpg_vis
fashionWOMENBlouses_Shirtsid0000337203_3back jpg___fashionWOMENBlouses_Shirtsid0000337203_2side jpg_vis

thanks a lot for your help to make research reproducible

Hi Hao

I'm trying to download the images but keep getting the following error:

Forbidden

You don't have permission to access /~hao.tang/uploads/models/XingGAN/ on this server.
Apache/2.4. .... Server at disi.unitn.it Port 80

May you please help where might be the problem?

Hi,
Thanks for your great work.
When I load the deepfashion dataset, I found the size of image is 256X256 but the size of the heat map is 256X176.
How can I use this data? Do I need to resize the image?