Direct Multi-view Multi-person 3D Human Pose Estimation

Related tags

Deep Learningmvp
Overview

Implementation of NeurIPS-2021 paper: Direct Multi-view Multi-person 3D Human Pose Estimation

[paper] [video-YouTube, video-Bilibili] [slides]

This is the official implementation of our NeurIPS-2021 work: Multi-view Pose Transformer (MvP). MvP is a simple algorithm that directly regresses multi-person 3D human pose from multi-view images.

Framework

mvp_framework

Example Result

mvp_framework

Reference

@article{wang2021mvp,
  title={Direct Multi-view Multi-person 3D Human Pose Estimation},
  author={Tao Wang and Jianfeng Zhang and Yujun Cai and Shuicheng Yan and Jiashi Feng},
  journal={Advances in Neural Information Processing Systems},
  year={2021}
}

1. Installation

  1. Set the project root directory as ${POSE_ROOT}.
  2. Install all the required python packages (with requirements.txt).
  3. compile deformable operation for projective attention.
cd ./models/ops
sh ./make.sh

2. Data and Pre-trained Model Preparation

2.1 CMU Panoptic

Please follow VoxelPose to download the CMU Panoptic Dataset and PoseResNet-50 pre-trained model.

The directory tree should look like this:

${POSE_ROOT}
|-- models
|   |-- pose_resnet50_panoptic.pth.tar
|-- data
|   |-- panoptic
|   |   |-- 16060224_haggling1
|   |   |   |-- hdImgs
|   |   |   |-- hdvideos
|   |   |   |-- hdPose3d_stage1_coco19
|   |   |   |-- calibration_160224_haggling1.json
|   |   |-- 160226_haggling1
|   |   |-- ...

2.2 Shelf/Campus

Please follow VoxelPose to download the Shelf/Campus Dataset.

Due to the limited and incomplete annotations of the two datasets, we use psudo ground truth 3D pose generated from VoxelPose to train the model, we expect mvp would perform much better with absolute ground truth pose data.

Please use voxelpose or other methods to generate psudo ground truth for the training set, you can also use our generated psudo GT: psudo_gt_shelf. psudo_gt_campus. psudo_gt_campus_fix_gtmorethanpred.

Due to the small dataset size, we fine-tune Panoptic pre-trained model to Shelf and Campus. Download the pretrained MvP on Panoptic from model_best_5view and model_best_3view_horizontal_view or model_best_3view_2horizon_1lookdown

The directory tree should look like this:

${POSE_ROOT}
|-- models
|   |-- model_best_5view.pth.tar
|   |-- model_best_3view_horizontal_view.pth.tar
|   |-- model_best_3view_2horizon_1lookdown.pth.tar
|-- data
|   |-- Shelf
|   |   |-- Camera0
|   |   |-- ...
|   |   |-- Camera4
|   |   |-- actorsGT.mat
|   |   |-- calibration_shelf.json
|   |   |-- pesudo_gt
|   |   |   |-- voxelpose_pesudo_gt_shelf.pickle
|   |-- CampusSeq1
|   |   |-- Camera0
|   |   |-- Camera1
|   |   |-- Camera2
|   |   |-- actorsGT.mat
|   |   |-- calibration_campus.json
|   |   |-- pesudo_gt
|   |   |   |-- voxelpose_pesudo_gt_campus.pickle
|   |   |   |-- voxelpose_pesudo_gt_campus_fix_gtmorethanpred_case.pickle

2.3 Human3.6M dataset

Please follow CHUNYUWANG/H36M-Toolbox to prepare the data.

2.4 Full Directory Tree

The data and pre-trained model directory tree should look like this, you can only download the Panoptic dataset and PoseResNet-50 for reproducing the main MvP result and ablation studies:

${POSE_ROOT}
|-- models
|   |-- pose_resnet50_panoptic.pth.tar
|   |-- model_best_5view.pth.tar
|   |-- model_best_3view_horizontal_view.pth.tar
|   |-- model_best_3view_2horizon_1lookdown.pth.tar
|-- data
|   |-- pesudo_gt
|   |   |-- voxelpose_pesudo_gt_shelf.pickle
|   |   |-- voxelpose_pesudo_gt_campus.pickle
|   |   |-- voxelpose_pesudo_gt_campus_fix_gtmorethanpred_case.pickle
|   |-- panoptic
|   |   |-- 16060224_haggling1
|   |   |   |-- hdImgs
|   |   |   |-- hdvideos
|   |   |   |-- hdPose3d_stage1_coco19
|   |   |   |-- calibration_160224_haggling1.json
|   |   |-- 160226_haggling1
|   |   |-- ...
|   |-- Shelf
|   |   |-- Camera0
|   |   |-- ...
|   |   |-- Camera4
|   |   |-- actorsGT.mat
|   |   |-- calibration_shelf.json
|   |   |-- pesudo_gt
|   |   |   |-- voxelpose_pesudo_gt_shelf.pickle
|   |-- CampusSeq1
|   |   |-- Camera0
|   |   |-- Camera1
|   |   |-- Camera2
|   |   |-- actorsGT.mat
|   |   |-- calibration_campus.json
|   |   |-- pesudo_gt
|   |   |   |-- voxelpose_pesudo_gt_campus.pickle
|   |   |   |-- voxelpose_pesudo_gt_campus_fix_gtmorethanpred_case.pickle
|   |-- HM36

3. Training and Evaluation

The evaluation result will be printed after every epoch, the best result can be found in the log.

3.1 CMU Panoptic dataset

We train and validate on the five selected camera views. We trained our models on 8 GPUs and batch_size=1 for each GPU, note the total iteration per epoch should be 3205, if not, please check your data.

python -m torch.distributed.launch --nproc_per_node=8 --use_env run/train_3d.py --cfg configs/panoptic/best_model_config.yaml

Pre-trained models

Datasets AP25 AP25 AP25 AP25 MPJPE pth
Panoptic 92.3 96.6 97.5 97.7 15.8 here

3.1.1 Ablation Experiments

You can find several ablation experiment configs under ./configs/panoptic/, for example, removing RayConv:

python -m torch.distributed.launch --nproc_per_node=8 --use_env run/train_3d.py --cfg configs/panoptic/ablation_remove_rayconv.yaml

3.2 Shelf/Campus datasets

As shelf/campus are very small dataset with incomplete annotation, we finetune pretrained MvP with pseudo ground truth 3D pose extracted with VoxelPose, we expect more accurate GT would help MvP achieve much higher performance.

python -m torch.distributed.launch --nproc_per_node=8 --use_env run/train_3d.py --cfg configs/shelf/mvp_shelf.yaml

Pre-trained models

Datasets Actor 1 Actor 2 Actor 2 Average pth
Shelf 99.3 95.1 97.8 97.4 here
Campus 98.2 94.1 97.4 96.6 here

3.3 Human3.6M dataset

MvP also applies to the naive single-person setting, with dataset like Human3.6, to come

python -m torch.distributed.launch --nproc_per_node=8 --use_env run/train_3d.py --cfg configs/h36m/mvp_h36m.yaml

4. Evaluation Only

To evaluate a trained model, pass the config and model pth:

python -m torch.distributed.launch --nproc_per_node=8 --use_env run/validate_3d.py --cfg xxx --model_path xxx

LICENSE

This repo is under the Apache-2.0 license. For commercial use, please contact the authors.

Owner
Sea AI Lab
Sea AI Lab
GitHub Repository
Codes for CyGen, the novel generative modeling framework proposed in "On the Generative Utility of Cyclic Conditionals" (NeurIPS-21)

On the Generative Utility of Cyclic Conditionals This repository is the official implementation of "On the Generative Utility of Cyclic Conditionals"

Chang Liu 44 Nov 16, 2022
Age and Gender prediction using Keras

cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span

XN3UR0N 58 May 03, 2022
Implementation of Basic Machine Learning Algorithms on small datasets using Scikit Learn.

Basic Machine Learning Algorithms All the basic Machine Learning Algorithms are implemented in Python using libraries Acknowledgements Machine Learnin

Piyal Banik 47 Oct 16, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
Baseline for the Spoofing-aware Speaker Verification Challenge 2022

Introduction This repository contains several materials that supplements the Spoofing-Aware Speaker Verification (SASV) Challenge 2022 including: calc

40 Dec 28, 2022
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds DLO is a lightweight and computationally-efficient frontend LiDAR odometry solution w

VECTR at UCLA 369 Dec 30, 2022
Neural models of common sense. 🤖

Unicorn on Rainbow Neural models of common sense. This repository is for the paper: Unicorn on Rainbow: A Universal Commonsense Reasoning Model on a N

AI2 60 Jan 05, 2023
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

SeaLion is designed to teach today's aspiring ml-engineers the popular machine learning concepts of today in a way that gives both intuition and ways of application. We do this through concise algori

Anish 324 Dec 27, 2022
A model which classifies reviews as positive or negative.

SentiMent Analysis In this project I built a model to classify movie reviews fromn the IMDB dataset of 50K reviews. WordtoVec : Neural networks only w

Rishabh Bali 2 Feb 09, 2022
Isaac Gym Reinforcement Learning Environments

Isaac Gym Reinforcement Learning Environments

NVIDIA Omniverse 714 Jan 08, 2023
Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

12 Jan 13, 2022
A Python Package for Portfolio Optimization using the Critical Line Algorithm

PyCLA A Python Package for Portfolio Optimization using the Critical Line Algorithm Getting started To use PyCLA, clone the repo and install the requi

19 Oct 11, 2022
End-To-End Memory Network using Tensorflow

MemN2N Implementation of End-To-End Memory Networks with sklearn-like interface using Tensorflow. Tasks are from the bAbl dataset. Get Started git clo

Dominique Luna 339 Oct 27, 2022
A Python library for common tasks on 3D point clouds

Point Cloud Utils (pcu) - A Python library for common tasks on 3D point clouds Point Cloud Utils (pcu) is a utility library providing the following fu

Francis Williams 622 Dec 27, 2022
Efficient electromagnetic solver based on rigorous coupled-wave analysis for 3D and 2D multi-layered structures with in-plane periodicity

Efficient electromagnetic solver based on rigorous coupled-wave analysis for 3D and 2D multi-layered structures with in-plane periodicity, such as gratings, photonic-crystal slabs, metasurfaces, surf

Alex Song 17 Dec 19, 2022
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

Documentation | FAQ | Release Notes | Roadmap | MACE Model Zoo | Demo | Join Us | 中文 Mobile AI Compute Engine (or MACE for short) is a deep learning i

Xiaomi 4.7k Dec 29, 2022
Official repository of the paper Privacy-friendly Synthetic Data for the Development of Face Morphing Attack Detectors

SMDD-Synthetic-Face-Morphing-Attack-Detection-Development-dataset Official repository of the paper Privacy-friendly Synthetic Data for the Development

10 Dec 12, 2022
Fully Convolutional DenseNet (A.K.A 100 layer tiramisu) for semantic segmentation of images implemented in TensorFlow.

FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th

Hasnain Raza 121 Oct 12, 2022
Generative Flow Networks

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Implementation for our paper, submitted to NeurIPS 2021 (also chec

Emmanuel Bengio 381 Jan 04, 2023
Official Repository for "Robust On-Policy Data Collection for Data Efficient Policy Evaluation" (NeurIPS 2021 Workshop on OfflineRL).

Robust On-Policy Data Collection for Data-Efficient Policy Evaluation Source code of Robust On-Policy Data Collection for Data-Efficient Policy Evalua

Autonomous Agents Research Group (University of Edinburgh) 2 Oct 09, 2022