Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.

• Project page
• Paper

Clone the repository with submodules:

git clone --recurse-submodules https://github.com/benjiebob/WLDO

For segmentation decoding, install pycocotools python -m pip install "git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI"

Follow the instructions on the repository to download StanfordExtra annotations and images.

Please ensure you have StanfordExtra_v12 installed, which we released 1 Feb 2021.

You may also wish to evaluate the Animal Pose Dataset. If so, download all of the dog images into data/animal_pose/images. For example, an image path should look like: data/animal_pose/images/2007_000063.jpg. We have reformatted the annotation file and enclose it in this repository data/animal_pose/animal_pose_data.json.

The train/validation/test splits used for our ECCV 2020 submission are contained in the data/StanfordExtra_v12 repository and under the data/animal_pose folder.

Please download our pretrained model and place underneath data/pretrained/3501_00034_betas_v4.pth.

To run the model on a series of images, place the images in a directory, and call the script demo.py. To see an example of this working, run demo.py and it will use the images in example_imgs:

cd wldo_regressor
python demo.py

To evaluate the performance of the model on the StanfordExtra dataset, run eval.py:

cd wldo_regressor
python eval.py --dataset stanford

You can also run on the animal_pose dataset

python eval.py --dataset animal_pose

Note that we have recently updated the tables in the arxiv version of our paper to account for some fixed dataset annotations and to use an improved version of the PCK metric. More details can be found in the paper.

This repository owes a great deal to the following works and authors:

• SMALify; Biggs et al. provided an energy minimization framework for fitting to animal video/images. A version of this was used as a baseline in this paper.
• SMAL; Zuffi et al. designed the SMAL deformable quadruped template model and have provided me with wonderful advice/guidance throughout my PhD journey.
• SMALST; Zuffi et al. provided PyTorch implementations of the SMAL skinning functions which have been used here.

If you make use of this code, please cite the following paper:

@inproceedings{biggs2020wldo,
  title={{W}ho left the dogs out?: {3D} animal reconstruction with expectation maximization in the loop},
  author={Biggs, Benjamin and Boyne, Oliver and Charles, James and Fitzgibbon, Andrew and Cipolla, Roberto},
  booktitle={ECCV},
  year={2020}
}

Please create a pull request or submit an issue if you would like to contribute.

(c) Benjamin Biggs, Oliver Boyne, Andrew Fitzgibbon and Roberto Cipolla. Department of Engineering, University of Cambridge 2020

By downloading this dataset, you agree to the Creative Commons Attribution-NonCommercial 4.0 International license. This license allows users to use, share and adapt the dataset, so long as credit is given to the authors (e.g. by citation) and the dataset is not used for any commercial purposes.

THIS SOFTWARE AND ANNOTATIONS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.