Code for "3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop"

Overview

PyMAF

This repository contains the code for the following paper:

3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Hongwen Zhang*, Yating Tian*, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, Zhenan Sun

* Equal contribution

[Project Page] [ArXiv] [Paper]

PyMAF

Requirements

  • Python 3.6.10

packages

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

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

Demo

[UPDATE] You can first give it a try on Google Colab using the notebook we have prepared, which is no need to prepare the environment yourself: Open In Colab

Run the demo code.

python3 demo.py --checkpoint=data/pretrained_model/PyMAF_model_checkpoint.pt --vid_file ./flashmob.mp4


Frame by frame reconstruction. Video clipped from here.

Evaluation

Human3.6M / 3DPW

Run the evaluation code. Using --dataset to specify the evaluation dataset.

# Example usage:

# Human3.6M Protocol 2
python3 eval.py --checkpoint=data/pretrained_model/PyMAF_model_checkpoint.pt --dataset=h36m-p2 --log_freq=20

# 3DPW
python3 eval.py --checkpoint=data/pretrained_model/PyMAF_model_checkpoint.pt --dataset=3dpw --log_freq=20

COCO Keypoint Localization

  1. Download the preprocessed data coco_2014_val.npz. Put it into the ./data/dataset_extras directory.

  2. Run the COCO evaluation code.

python3 eval_coco.py --checkpoint=data/pretrained_model/PyMAF_model_checkpoint.pt

Training

To perform training, we need to collect preprocessed files of training datasets at first.

The preprocessed labels have the same format as SPIN and can be retrieved from here. Please refer to SPIN for more details about data preprocessing.

PyMAF is trained on Human3.6M at the first stage and then trained on the mixture of both 2D and 3D datasets at the second stage. Example usage:

# training on Human3.6M
python3 train.py --regressor pymaf_net --single_dataset --misc TRAIN.BATCH_SIZE 64
# training on mixed datasets
python3 train.py --regressor pymaf_net --pretrained_checkpoint path/to/checkpoint_file.pt --misc TRAIN.BATCH_SIZE 64

Running the above commands will use Human3.6M or mixed datasets for training, respectively. We can monitor the training process by setting up a TensorBoard at the directory ./logs.

Citation

If this work is helpful in your research, please cite the following paper.

@article{pymaf2021,
  title={3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop},
  author={Zhang, Hongwen and Tian, Yating and Zhou, Xinchi and Ouyang, Wanli and Liu, Yebin and Wang, Limin and Sun, Zhenan},
  journal={arXiv preprint arXiv:2103.16507},
  year={2021}
}

Acknowledgments

The code is developed upon the following projects. Many thanks to their contributions.

Owner
Hongwen Zhang
Hongwen Zhang
Python package for dynamic system estimation of time series

PyDSE Toolset for Dynamic System Estimation for time series inspired by DSE. It is in a beta state and only includes ARMA models right now. Documentat

Blue Yonder GmbH 40 Oct 07, 2022
RETRO-pytorch - Implementation of RETRO, Deepmind's Retrieval based Attention net, in Pytorch

RETRO - Pytorch (wip) Implementation of RETRO, Deepmind's Retrieval based Attent

Phil Wang 556 Jan 04, 2023
Code for our NeurIPS 2021 paper Mining the Benefits of Two-stage and One-stage HOI Detection

CDN Code for our NeurIPS 2021 paper "Mining the Benefits of Two-stage and One-stage HOI Detection". Contributed by Aixi Zhang*, Yue Liao*, Si Liu, Mia

71 Dec 14, 2022
TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

tf-metal-experiments TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) Setup This is tested on M1 series Apple Silicon SOC only. Te

Timothy Liu 161 Jan 03, 2023
Image reconstruction done with untrained neural networks.

PyTorch Deep Image Prior An implementation of image reconstruction methods from Deep Image Prior (Ulyanov et al., 2017) in PyTorch. The point of the p

Atiyo Ghosh 192 Nov 30, 2022
Nonnegative spatial factorization for multivariate count data

Nonnegative spatial factorization for multivariate count data This repository contains supporting code to facilitate reproducible analysis. For detail

Will Townes 24 Dec 19, 2022
Meta-learning for NLP

Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks Code for training the meta-learning models and fine-tuning on downstr

IESL 43 Nov 08, 2022
The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:

9 Nov 14, 2022
Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Storchastic is a PyTorch library for stochastic gradient estimation in Deep Learning

Emile van Krieken 140 Dec 30, 2022
A Demo server serving Bert through ONNX with GPU written in Rust with <3

Demo BERT ONNX server written in rust This demo showcase the use of onnxruntime-rs on BERT with a GPU on CUDA 11 served by actix-web and tokenized wit

Xavier Tao 28 Jan 01, 2023
The official implementation of the Hybrid Self-Attention NEAT algorithm

PUREPLES - Pure Python Library for ES-HyperNEAT About This is a library of evolutionary algorithms with a focus on neuroevolution, implemented in pure

Adrian Westh 91 Dec 12, 2022
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation Paper Multi-Target Adversarial Frameworks for Domain Adaptation in

Valeo.ai 20 Jun 21, 2022
MG-GCN: Scalable Multi-GPU GCN Training Framework

MG-GCN MG-GCN: multi-GPU GCN training framework. For more information, please read our paper. After cloning our repository, run git submodule update -

Translational Data Analytics (TDA) Lab @GaTech 6 Oct 24, 2022
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 04, 2022
A unet implementation for Image semantic segmentation

Unet-pytorch a unet implementation for Image semantic segmentation 参考网上的Unet做分割的代码,做了一个针对kaggle地盐识别的,请去以下地址获取数据集: https://www.kaggle.com/c/tgs-salt-id

Rabbit 3 Jun 29, 2022
Code release for our paper, "SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo"

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

68 Dec 14, 2022
Gradient representations in ReLU networks as similarity functions

Gradient representations in ReLU networks as similarity functions by Dániel Rácz and Bálint Daróczy. This repo contains the python code related to our

1 Oct 08, 2021
BabelCalib: A Universal Approach to Calibrating Central Cameras. In ICCV (2021)

BabelCalib: A Universal Approach to Calibrating Central Cameras This repository contains the MATLAB implementation of the BabelCalib calibration frame

Yaroslava Lochman 55 Dec 30, 2022
PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR)

Vision Transformer for Fast and Efficient Scene Text Recognition (ICDAR 2021) ViTSTR is a simple single-stage model that uses a pre-trained Vision Tra

Rowel Atienza 198 Dec 27, 2022