Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

Related tags

Deep Learningcomputer-visioncamera-calibrationcomputer-graphicshuman-pose-estimation3d-human-pose3d-human-mesh3d-human-shape-and-pose-estimation3d-human-reconstruction
Overview

SPEC: Seeing People in the Wild with an Estimated Camera [ICCV 2021]

Open In Colab report report

SPEC: Seeing People in the Wild with an Estimated Camera,
Muhammed Kocabas, Chun-Hao Paul Huang, Joachim Tesch, Lea Müller, Otmar Hilliges, Michael J. Black,
International Conference on Computer Vision (ICCV), 2021

Features

SPEC is a camera-aware human body pose and shape estimation method. It both predicts the camera parameters and SMPL body model for a given image. CamCalib predicts the camera parameters. SPEC uses these parameters to predict SMPL body model parameters.

This implementation:

  • has the demo code for SPEC and CamCalib implemented in PyTorch.
  • achieves SOTA results in SPEC-SYN and SPEC-MTP datasets.
  • shows how to perform evaluation on SPEC-SYN and SPEC-MTP datasets.

Updates

  • 13/10/2021: Demo and evaluation code is released.

Getting Started

SPEC has been implemented and tested on Ubuntu 18.04 with python >= 3.7. If you don't have a suitable device, try running our Colab demo.

Clone the repo:

git clone https://github.com/mkocabas/SPEC.git

Install the requirements using virtualenv or conda:

# pip
source scripts/install_pip.sh

# conda
source scripts/install_conda.sh

Running the Demo

SPEC

First, you need to download the required data (i.e our trained model and SMPL model parameters). It is approximately 1GB. To do this you can just run:

source scripts/prepare_data.sh

Then, running the demo is as simple as:

python scripts/spec_demo.py \
  --image_folder data/sample_images \
  --output_folder logs/spec/sample_images

Sample demo output:

Here the green line is the horizon obtained using estimated camera parameters. On the right, the ground plane is visualized to show how accurate the global translation is.

CamCalib

If you are only interested in estimating the camera parameters of an image, run the CamCalib demo:

python scripts/camcalib_demo.py \
  --img_folder <input image folder> \
  --out_folder <output folder> \
  --show # visualize the raw network predictions

This script outputs a pickle file which contains the predicted camera parameters for each input image along with an output image which visualizes the camera parameters as a horizon line. Pickle file contains:

'vfov' : vertical field of view in radians
'f_pix': focal length in pixels
'pitch': pitch in radians
'roll' : roll in radians

Google Colab

Training

Training instructions will follow soon.

Datasets

Pano360, SPEC-MTP, and SPEC-SYN are new datasets introduced in our paper. You can download them from the Downloads section of our project page.

For Pano360 dataset, we have released the Flickr image ids which can be used to download images using FlickrAPI. We have provided a download script in this repo. Some of the images will be missing due to users deleting their photos. In this case, you can also use scrape_and_download function provided in the script to find and download more photos.

After downloading the SPEC-SYN, SPEC-MTP, Pano360, and 3DPW datasets, the data folder should look like:

data/
├── body_models
│   └── smpl
├── camcalib
│   └── checkpoints
├── dataset_extras
├── dataset_folders
│   ├── 3dpw
│   ├── pano360
│   ├── spec-mtp
│   └── spec-syn
├── sample_images
└── spec
    └── checkpoints

Evaluation

You can evaluate SPEC on SPEC-SYN, SPEC-MTP, and 3DPW datasets by running:

python scripts/spec_eval.py \
  --cfg data/spec/checkpoints/spec_config.yaml \
  --opts DATASET.VAL_DS spec-syn_spec-mtp_3dpw-test-cam

Running this script should give results reported in this table:

W-MPJPE PA-MPJPE W-PVE
SPEC-MTP 124.3 71.8 147.1
SPEC-SYN 74.9 54.5 90.5
3DPW 106.7 53.3 124.7

Citation

@inproceedings{SPEC:ICCV:2021,
  title = {{SPEC}: Seeing People in the Wild with an Estimated Camera},
  author = {Kocabas, Muhammed and Huang, Chun-Hao P. and Tesch, Joachim and M\"uller, Lea and Hilliges, Otmar and Black, Michael J.},
  booktitle = {Proc. International Conference on Computer Vision (ICCV)},
  pages = {11035--11045},
  month = oct,
  year = {2021},
  doi = {},
  month_numeric = {10}
}

License

This code is available for non-commercial scientific research purposes as defined in the LICENSE file. By downloading and using this code you agree to the terms in the LICENSE. Third-party datasets and software are subject to their respective licenses.

References

We indicate if a function or script is borrowed externally inside each file. Here are some great resources we benefit:

Consider citing these works if you use them in your project.

Contact

For questions, please contact [email protected]

For commercial licensing (and all related questions for business applications), please contact [email protected].

Comments
  • Translation of the camera

    Translation of the camera

    I'd like to know if it's possible to get a translation of the camera. I've found the code just for rotation and focal length estimation.

    On this image https://github.com/mkocabas/SPEC/blob/master/docs/assets/spec_gif.gif the camera is placed in the world space somehow. How did you do this? Thanks.

    opened by Dene33 4
  • Segmentation fault (core dumped)

    Segmentation fault (core dumped)

    Hi~ When I run the command python scripts/spec_demo.py --batch_size 1 --image_folder data/sample_images --output_folder logs/spec/sample_images, I got the error as this: Segmentation fault (core dumped) I have no idea about it, could you help me, please?

    opened by JinkaiZheng 2
  • There is no flickr_photo_ids.npy

    There is no flickr_photo_ids.npy

    Hello author:

    After unzipping the spec-github-data.zip, there is no data/dataset_folders/pano360/flickr_photo_ids.npy. I don't know whether I find the right place, could you help me?

    Best regards.

    opened by songxujay 1
  • video demo

    video demo

    It's a great job. Is the video demo not available now? `def main(args):

    demo_mode = args.mode

if demo_mode == 'video':
    raise NotImplementedError
elif demo_mode == 'webcam':
    raise NotImplementedError`
    opened by FatherPrime 1
  • Fix PARE requirement in requirement.txt

    Fix PARE requirement in requirement.txt

    Thanks for sharing this work! Please fix the requirement in requirement.txt file of PARE. It should be (currently missing "git+" prefix): git+https://github.com/mkocabas/PARE.git

    opened by Omri-CG 1
  • get error horizon line by camcalib_demo.py

    get error horizon line by camcalib_demo.py

    all example_image seem get error result by camcalib_demo.py, look like this: COCO_val2014_000000326555 it seems the predicted horizon line shift by model,how should I solve this problem？

    opened by hhhlllyyy 0
  • feet fit accuracy

    feet fit accuracy

    I've encountered frequent problems that SPEC is having to fit the model to feet correctly:

    image image

    Is this the same problem as mentioned in the VIBE discussion: https://github.com/mkocabas/VIBE/issues/24#issuecomment-596714558 ? Have you tried to include the OpenPose feet keypoint predictions to the loss?

    Thanks for the great work!

    opened by smidm 0
  • Dataset npz interpretation

    Dataset npz interpretation

    Dear Muhammed,

    Thank you for this work and the datasets! I'm trying to interpret the data in the SPEC-SYN npz data files, but I'm not sure what each key means. Is there documentation? They are the following: ['imgname', 'center', 'scale', 'pose', 'shape', 'part', 'mmpose_keypoints', 'openpose', 'openpose_gt', 'S', 'focal_length', 'cam_rotmat', 'cam_trans', 'cam_center', 'cam_pitch', 'cam_roll', 'cam_hfov', 'cam_int', 'camcalib_pitch', 'camcalib_roll', 'camcalib_vfov', 'camcalib_f_pix']

    At this point, I'd just like to plot the 24 SMPL joints on the image. Based on the array shape, I assume 'S' contains the joints. Is cam_rotmat the rotation from world space to camera space? Is the cam_trans the position of the camera or the top right part of the extrinsic matrix? I assume for this plotting exercise I can ignore everything except S, cam_rotmat, cam_trans and cam_int. Still for some reason the points end up at at very wrong places. Maybe 'S' is something else? Or am I using the camera params wrong?

    Thanks! Istvan

    opened by isarandi 5
  • Colab notebook inaccesible

    Colab notebook inaccesible

    Hi,I tried accesing the colab notebook but wasn't able to access it.Would you be able to kindly suggest another way to run inference as I am currently working on a windows based CPU system.

    opened by sparshgarg23 0
  • Error during single dataset evaluation

    Error during single dataset evaluation

    I tried running the evaluation only for the 3DPW dataset with the following command:

    python scripts/spec_eval.py --cfg data/spec/checkpoints/spec_config.yaml --opts DATASET.VAL_DS 3dpw-test-cam

    But it gives the following error:

    TypeError: test_step() missing 1 required positional argument: 'dataloader_nb'

    Fixed it by giving a default value for dataloader_nb for the test_step function in trainer.py:

    def test_step(self, batch, batch_nb, dataloader_nb=0):
    return self.validation_step(batch, batch_nb, dataloader_nb)

    The error occurs because we don't append dataloader_idx to args in evaluation_loop.py:

    if multiple_test_loaders or multiple_val_loaders:
    args.append(dataloader_idx)
    opened by umariqb 1
Owner
Muhammed Kocabas
Muhammed Kocabas
GitHub Repository
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022
[Arxiv preprint] Causality-inspired Single-source Domain Generalization for Medical Image Segmentation (code&data-processing pipeline)

Causality-inspired Single-source Domain Generalization for Medical Image Segmentation Arxiv preprint Repository under construction. Might still be bug

Cheng 31 Dec 27, 2022
Using PyTorch Perform intent classification using three different models to see which one is better for this task

Using PyTorch Perform intent classification using three different models to see which one is better for this task

Yoel Graumann 1 Feb 14, 2022
Long Expressive Memory (LEM)

Long Expressive Memory for Sequence Modeling This repository contains the implementation to reproduce the numerical experiments of the paper Long Expr

Konstantin Rusch 47 Dec 17, 2022
Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees" Installa

0 Oct 13, 2021
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
An open source object detection toolbox based on PyTorch

MMDetection is an open source object detection toolbox based on PyTorch. It is a part of the OpenMMLab project.

Bo Chen 24 Dec 28, 2022
Code repository for paper `Skeleton Merger: an Unsupervised Aligned Keypoint Detector`.

Skeleton Merger Skeleton Merger, an Unsupervised Aligned Keypoint Detector. The paper is available at https://arxiv.org/abs/2103.10814. A map of the r

北海若 48 Nov 14, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
Training Very Deep Neural Networks Without Skip-Connections

DiracNets v2 update (January 2018): The code was updated for DiracNets-v2 in which we removed NCReLU by adding per-channel a and b multipliers without

Sergey Zagoruyko 585 Oct 12, 2022
3D detection and tracking viewer (visualization) for kitti & waymo dataset

3D detection and tracking viewer (visualization) for kitti & waymo dataset

222 Jan 08, 2023
classify fashion-mnist dataset with pytorch

Fashion-Mnist Classifier with PyTorch Inference 1- clone this repository: git clone https://github.com/Jhamed7/Fashion-Mnist-Classifier.git 2- Instal

1 Jan 14, 2022
Official PyTorch Implementation of Rank & Sort Loss [ICCV2021]

Rank & Sort Loss for Object Detection and Instance Segmentation The official implementation of Rank & Sort Loss. Our implementation is based on mmdete

Kemal Oksuz 229 Dec 20, 2022
Tilted Empirical Risk Minimization (ICLR '21)

Tilted Empirical Risk Minimization This repository contains the implementation for the paper Tilted Empirical Risk Minimization ICLR 2021 Empirical ri

Tian Li 40 Nov 28, 2022
Generalized and Efficient Blackbox Optimization System.

OpenBox Doc | OpenBox中文文档 OpenBox: Generalized and Efficient Blackbox Optimization System OpenBox is an efficient and generalized blackbox optimizatio

DAIR Lab 238 Dec 29, 2022
Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Wilson 1.7k Dec 30, 2022
Code for Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks Under construction. Description Code for Phase diagram of S

Rodrigo Veiga 3 Nov 24, 2022
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集，准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022
3D ResNets for Action Recognition (CVPR 2018)

3D ResNets for Action Recognition Update (2020/4/13) We published a paper on arXiv. Hirokatsu Kataoka, Tenga Wakamiya, Kensho Hara, and Yutaka Satoh,

Kensho Hara 3.5k Jan 06, 2023
This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset

HiRID-ICU-Benchmark This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset for which the manuscript can be found here.

Biomedical Informatics at ETH Zurich 30 Dec 16, 2022