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PyTorch implementation of CloudWalk's recent work DenseBody

Results 13 densebody_pytorch issues
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Hi Lotayou, thanks for your project and nice instructions. I just want to ask that do you have the code to process the H36M downloaded from the original website. It...

Hi @Lotayou, thanks for your great effort! But I found the link to pretrained encoder and decoder cannot be reached. Would you mind reupload them again? Thanks!

hi, I modify the train.sh as : ``` python train.py --name resnet_radvani_32000_20190415 --model resnet --netD conv-up --batch_size 4 --max_dataset_size 32000 --niter 20 --niter_decay 50 --save_result_freq 250 --save_epoch_freq 2 --ndown 6...

I follow the step to make an .obj file from basicModel_m_lbs_10_207_0_v1.0.2.fbx but i got this error ![image](https://user-images.githubusercontent.com/49749924/62623176-d3993200-b952-11e9-8a95-c547a51d0200.png) so i change the dimension of the verts but i get another error...

Hi, Once i have the UV maps for the images after using create_uv_maps.py , how do i construct a SMPL model out of it?

@Lotayou (a.k.a myself) I just found that my SMPL UV data at hand is messed up, UV vertices' topology does not match that of SMPL 3D mesh. Here's the result:...

help wanted
good first issue

Hi Lotayou, Thanks for your work. I'm following your steps to prepare my human3.6m data. I have got data washed successfully thanks to your script. However, in PREPS.md - Create...

I am not sure if the smpl_fbx_template.obj file I generated is correct. ![image](https://user-images.githubusercontent.com/38310357/68951091-5eae8180-07f8-11ea-900a-495713370ffd.png) ![image](https://user-images.githubusercontent.com/38310357/68951046-50f8fc00-07f8-11ea-83b3-7248de58288a.png) ![image](https://user-images.githubusercontent.com/38310357/68951211-93223d80-07f8-11ea-9a8f-92205df6fd7b.png) After I run uv_map_generator.py, I get an error. ValueError: shape mismatch: value array of shape...

Hello, i have tried your code and make some changes(tips) to perform better. Hope it would help: 1. L1 loss weight . The L1 loss weight generated is wrong accrodding...

Can I implement the project for CUDA-16.04?