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

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:

• Pano360 dataset preprocessing is borrowed from Single View Metrology in the Wild.
• Most of the utility functions depends on our another paper PARE.
• Some functions are borrowed from SPIN.

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

Contact

For questions, please contact spec@tue.mpg.de

For commercial licensing (and all related questions for business applications), please contact ps-licensing@tue.mpg.de.