SPIFu

Official Implementation of SPIFu (NeurIPS 2022 )

Update:

a) To download the pretrained model:

SPIFu trained on Groundtruth SMPL-X (Will follow the SMPL-X pose more closely): https://drive.google.com/file/d/1f6ZTysLvlq23II-pYwwo0lVDbAOHjlq-/view?usp=share_link
SPIFu trained on Predicted SMPL-X (More robust to errors in SMPL-X pose errors): https://drive.google.com/file/d/1gWWMIkrwYQwJaN9KnHBTL2-WTtqmu8wt/view?usp=share_link
Frontal Normal Map generator: https://drive.google.com/file/d/10_6w4DKODuzYxC88UgwPp5jHb6SPg7_5/view?usp=share_link
Rear Normal Map generator: https://drive.google.com/file/d/10FD3qNyGw6fajoBEsHMOLeehM_F63z4T/view?usp=share_link

Prerequisites:

1) Request permission to use THuman2.0 dataset (https://github.com/ytrock/THuman2.0-Dataset).

After permission granted, download the dataset (THuman2.0_Release). Put the "THuman2.0_Release" folder inside the "rendering_script" folder.

2) Rendering Images and Normal maps

Please refer to https://github.com/kcyt/IntegratedPIFu. Note that Depth maps are not required here.

3) Install smplx (Only if not using groundtruth smplx fittings from THuman2.0 dataset)

Please follow the installation instructions from smplx from https://github.com/vchoutas/smplx#installation. Then, use smplify-x (https://github.com/vchoutas/smplify-x) on the rendered RGB images from Step 2.

3) Run Ray-Based Sampling (RBS)

Run the script unroll_gt_smplx.py or unroll_predicted_smplx.py depending on whether you want to apply RBS on the groundtruth smplx fittings or the predicted smplx fittings from smplify-x.

4) Train the normal maps predicator, and then use it for generating predicted normal maps.

Run the script train_normalmodel.py. After the model is trained, run the script generatemaps_normalmodel.py to generated the predicted normal maps.

To Run :

Run the script train_smpl_unrolled.py. Configuration can be set in lib/options.py file.

Yuhuoo / SPIFu