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
One line to host them all. Bootstrap your image search case in minutes.

One line to host them all. Bootstrap your image search case in minutes. Survey NOW gives the world access to customized neural image search in just on

Jina AI 403 Dec 30, 2022
TAPEX: Table Pre-training via Learning a Neural SQL Executor

TAPEX: Table Pre-training via Learning a Neural SQL Executor The official repository which contains the code and pre-trained models for our paper TAPE

Microsoft 157 Dec 28, 2022
Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini!

ConversorDeMedidas_CapuccinoGelado Este conversor criará a medida exata para sua receita de capuccino gelado da grandiosa Rafaella Ballerini! Requirem

Arthur Ottoni Ribeiro 48 Nov 15, 2022
MIRACLE (Missing data Imputation Refinement And Causal LEarning)

MIRACLE (Missing data Imputation Refinement And Causal LEarning) Code Author: Trent Kyono This repository contains the code used for the "MIRACLE: Cau

van_der_Schaar \LAB 15 Dec 29, 2022
DeepGNN is a framework for training machine learning models on large scale graph data.

DeepGNN Overview DeepGNN is a framework for training machine learning models on large scale graph data. DeepGNN contains all the necessary features in

Microsoft 45 Jan 01, 2023
Two-stage CenterNet

Probabilistic two-stage detection Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network. Probabilistic two-st

Xingyi Zhou 1.1k Jan 03, 2023
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks

MEAL-V2 This is the official pytorch implementation of our paper: "MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tric

Zhiqiang Shen 653 Dec 19, 2022
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

Deep Vision and Graphics This repo supplements course "Deep Vision and Graphics" taught at YSDA @fall'21. The course is the successor of "Deep Learnin

Yandex School of Data Analysis 160 Jan 02, 2023
Numerai tournament example scripts using NN and optuna

numerai_NN_example Numerai tournament example scripts using pytorch NN, lightGBM and optuna https://numer.ai/tournament Performance of my model based

Takahiro Maeda 12 Oct 10, 2022
The Adapter-Bot: All-In-One Controllable Conversational Model

The Adapter-Bot: All-In-One Controllable Conversational Model This is the implementation of the paper: The Adapter-Bot: All-In-One Controllable Conver

CAiRE 37 Nov 04, 2022
Bio-OFC gym implementation and Gym-Fly environment

Bio-OFC gym implementation and Gym-Fly environment This repository includes the gym compatible implementation of the Bio-OFC algorithm from the paper

Siavash Golkar 1 Nov 16, 2021
GANSketchingJittor - Implementation of Sketch Your Own GAN in Jittor

GANSketching in Jittor Implementation of (Sketch Your Own GAN) in Jittor(计图). Or

Bernard Tan 10 Jul 02, 2022
Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Transformers Arabic licence plate recognition 🚗 Solution to the kaggle competition Machathon 3.0. Ranked in the top 6️⃣ at the final evaluation phase

Noran Hany 17 Dec 04, 2022
Source code of CIKM2021 Long Paper "PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling".

PSSL Source code of CIKM2021 Long Paper "PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling". It consists of the pre-tra

2 Dec 21, 2021
This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters.

openmc-plasma-source This python-based package offers a way of creating a parametric OpenMC plasma source from plasma parameters. The OpenMC sources a

Fusion Energy 10 Oct 18, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide.

SARS-CoV-2 processing requests Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide. Prerequisites This autom

useGalaxy.eu 17 Aug 13, 2022
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness (NeurIPS2021) This repository contains code for the paper "Smo

Jongheon Jeong 17 Dec 27, 2022