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