Code for "Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo"

Overview

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo

framework

This repository includes the source code for our CVPR 2021 paper on multi-view multi-person 3D pose estimation. Please read our paper for more details at https://arxiv.org/abs/2104.02273. The project webpage is available here.

Bibtex:

@InProceedings{Lin_2021_CVPR,
    author    = {Lin, Jiahao and Lee, Gim Hee},
    title     = {Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {11886-11895}
}

Environment

Our code is tested on

  • Python 3.8.5
  • PyTorch 1.6.0 & torchvision 0.7.0
  • CUDA 11.2

Preparing Data

Download following data before using the code in this repository:

The data should be organized as follows:

    ROOTDIR/
        └── data/
            └── Campus/
                └── actorsGT.mat
                └── calibration_campus.json
                └── pred_campus_maskrcnn_hrnet_coco.pkl
            └── Shelf/
                └── actorsGT.mat
                └── calibration_shelf.json
                └── pred_shelf_maskrcnn_hrnet_coco.pkl
            └── Panoptic/
                └── 160224_haggling1/
                └── 160226_haggling1/
                └── ...
                └── keypoints_train_results.json
                └── keypoints_validation_results.json
            └── panoptic_training_pose.pkl
        └── output/
            └── campus_synthetic/mvmppe/config/model_best_pretrained.pth.tar
            └── shelf_synthetic/mvmppe/config/model_best_pretrained.pth.tar
            └── panoptic/mvmppe/config/model_best_pretrained.pth.tar
        └── ...

Training and Inference

Below are the commands for training our model on different datasets.

The Campus dataset:

    python run/train.py --cfg configs/campus/config.yaml

The Shelf dataset:

    python run/train.py --cfg configs/shelf/config.yaml

The CMU Panoptic dataset:

    python run/train.py --cfg configs/panoptic/config.yaml

Below are the commands for performing inference with our pre-trained models.

The Campus dataset:

    python run/validate.py --cfg configs/campus/config.yaml -t pretrained

The Shelf dataset:

    python run/validate.py --cfg configs/shelf/config.yaml -t pretrained

The CMU Panoptic dataset:

    python run/validate.py --cfg configs/panoptic/config.yaml -t pretrained
Owner
Jiahao Lin
Jiahao Lin
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes

The Picasso Library is intended for complex real-world applications with large-scale surfaces, while it also performs impressively on the small-scale applications over synthetic shape manifolds. We h

97 Dec 01, 2022
Zeyuan Chen, Yangchao Wang, Yang Yang and Dong Liu.

Principled S2R Dehazing This repository contains the official implementation for PSD Framework introduced in the following paper: PSD: Principled Synt

zychen 78 Dec 30, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
Aspect-Sentiment-Multiple-Opinion Triplet Extraction (NLPCC 2021)

The code and data for the paper "Aspect-Sentiment-Multiple-Opinion Triplet Extraction" Requirements Python 3.6.8 torch==1.2.0 pytorch-transformers==1.

慢半拍 5 Jul 02, 2022
Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabri

224 Dec 29, 2022
Selene is a Python library and command line interface for training deep neural networks from biological sequence data such as genomes.

Selene is a Python library and command line interface for training deep neural networks from biological sequence data such as genomes.

Troyanskaya Laboratory 323 Jan 01, 2023
ULMFiT for Genomic Sequence Data

Genomic ULMFiT This is an implementation of ULMFiT for genomics classification using Pytorch and Fastai. The model architecture used is based on the A

Karl 276 Dec 12, 2022
Blind visual quality assessment on 360° Video based on progressive learning

Blind visual quality assessment on omnidirectional or 360 video (ProVQA) Blind VQA for 360° Video via Progressively Learning from Pixels, Frames and V

5 Jan 06, 2023
Python implementation of Wu et al (2018)'s registration fusion

reg-fusion Projection of a central sulcus probability map using the RF-ANTs approach (right hemisphere shown). This is a Python implementation of Wu e

Dan Gale 26 Nov 12, 2021
Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 07, 2023
BrainGNN - A deep learning model for data-driven discovery of functional connectivity

A deep learning model for data-driven discovery of functional connectivity https://doi.org/10.3390/a14030075 Usman Mahmood, Zengin Fu, Vince D. Calhou

Usman Mahmood 3 Aug 28, 2022
official implemntation for "Contrastive Learning with Stronger Augmentations"

CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun

Lab for MAchine Perception and LEarning (MAPLE) 47 Nov 29, 2022
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation

Unseen Object Clustering: Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation Introduction In this work, we propose a new method

NVIDIA Research Projects 132 Dec 13, 2022
Code and experiments for "Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities"

corn-ordinal-neuralnet This repository contains the orginal model code and experiment logs for the paper "Deep Neural Networks for Rank Consistent Ord

Raschka Research Group 14 Dec 27, 2022
YOLOX Win10 Project

Introduction 这是一个用于Windows训练YOLOX的项目,相比于官方项目,做了一些适配和修改: 1、解决了Windows下import yolox失败,No such file or directory: 'xxx.xml'等路径问题 2、CUDA out of memory等显存不

5 Jun 08, 2022
nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

6 Nov 22, 2022
SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article

Moore 34 Nov 03, 2022
Perturb-and-max-product: Sampling and learning in discrete energy-based models

Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur

Vicarious 2 Mar 14, 2022
Datasets, Transforms and Models specific to Computer Vision

vision Datasets, Transforms and Models specific to Computer Vision Installation First install the nightly version of OneFlow python3 -m pip install on

OneFlow 68 Dec 07, 2022