Kairobo/PaFF

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PaFF

This repository contains the code for the following paper:

Delving Deep into Pixel Alignment Feature for Accurate Multi-view Human Mesh Recovery
Kai Jia, Hongwen Zhang, Liang An, Yebin Liu

AAAI, 2023

[Project Page] | [Paper]

TODO List

Demo code and pretrained model
Code for evaluation
Code for training

Requirements

Python 3.6.10

packages

PyTorch tested on version 1.1.0
torchvision tested on version 0.3.0
Neural Renderer (render densepose labels for training)
opendr (visualization in training)
pyrender (optional for demo)
other packages listed in requirements.txt

necessary files

mesh_downsampling.npz & DensePose UV data

Run the following script to fetch mesh_downsampling.npz & DensePose UV data from other repositories.

bash fetch_data.sh

SMPL model files

Collect SMPL model files from https://smpl.is.tue.mpg.de and UP. Rename model files and put them into the ./data/smpl directory.

Fetch preprocessed data from SPIN.

Download the pre-trained model and put it into the ./data/pretrained_model directory.

After collecting the above necessary files, the directory structure of ./data is expected as follows.

./data
├── dataset_extras
│   └── .npz files
├── J_regressor_extra.npy
├── J_regressor_h36m.npy
├── mesh_downsampling.npz
├── pretrained_model
│   └── PyMAF_model_checkpoint.pt
├── smpl
│   ├── SMPL_FEMALE.pkl
│   ├── SMPL_MALE.pkl
│   └── SMPL_NEUTRAL.pkl
├── smpl_mean_params.npz
├── static_fits
│   └── .npy files
└── UV_data
    ├── UV_Processed.mat
    └── UV_symmetry_transforms.mat