Package: human-body-prior
VPoser: Variational Human Pose PriorDescriptionThe articulated 3D pose of the human body is high-dimensional and complex.
Many applications make use of a prior distribution over valid human poses, but modeling this distribution is difficult.
Here we provide a learned distribution trained from a large dataset of human poses represented as SMPL bodies.Here we present a method that is used inSMPLify-X.
Our variational human pose prior, named VPoser, has the following features:defines a prior of SMPL pose parametersis end-to-end differentiableprovides a way to penalize impossible poses while admitting valid oneseffectively models correlations among the joints of the bodyintroduces an efficient, low-dimensional, representation for human posecan be used to generate valid 3D human poses for data-dependent tasksTable of ContentsDescriptionInstallationLoading trained modelsTrain VPoserTutorialsCitationLicenseAcknowledgmentsContactInstallationTo install the model:To install from PyPi simply run:pipinstallhuman_body_priorClone this repository and install it using thesetup.pyscript:gitclonehttps://github.com/nghorbani/human_body_prior
pythonsetup.pyinstallLoading Trained ModelsTo download the trainedVPosermodels go to theSMPL-X project websiteand register to get access to the downloads section. Afterwards, you can follow themodel loading tutorialto load and use your trained VPoser models.Train VPoserWe train VPoser, using avariational autoencoderthat learns a latent representation of human pose and regularizes the distribution of the latent code to be a normal distribution.
We train our prior on data from theAMASSdataset;
specifically, the SMPL pose parameters of various publicly available human motion capture datasets.
You can follow thedata preparation tutorialto learn how to download and prepare AMASS for VPoser.
Afterwards, you cantrain VPoser from scratch.TutorialsVPoser PoZ Space for Body ModelsSampling Novel Body Poses from VPoserPreparing VPoser Training DatasetTrain VPoser from ScratchCitationPlease cite the following paper if you use this code directly or indirectly in your research/projects:@inproceedings{SMPL-X:2019,
  title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
  author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019}
}Also note that if you consider training your own VPoser for your research using the AMASS dataset,
then please follow its respective citation guideline.LicenseSoftware Copyright License fornon-commercial scientific research purposes.
Please read carefully theterms and conditionsand any accompanying documentation before you download and/or use the SMPL-X/SMPLify-X model, data and software, (the "Model & Software"), including 3D meshes, blend weights, blend shapes, textures, software, scripts, and animations. By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under thisLicense.ContactThe code in this repository is developed byNima Ghorbani.If you have any questions you can contact us atsmplx@tuebingen.mpg.de.For commercial licensing, contactps-licensing@tue.mpg.deAcknowledgmentsWe thank the authors ofAMASSfor their early release of their dataset for this project.
We thankPartha Ghoshfor disscussions and insights that helped with this project.