Curated List of 3D Morphable Model Software and Data

The idea of this list is to collect shared data and algorithms around 3D Morphable Models. You are invited to contribute to this list by adding a pull request. The original list arised from the Dagstuhl seminar on 3D Morphable Models https://www.dagstuhl.de/19102 in March 2019.

Morphable Models:

Google Group

• Google group to help foster a research community interested in 3DMMs, share resources etc.: https://groups.google.com/forum/#!forum/3d-morphable-models

Face Models:

• Basel Face Model 2009: http://faces.cs.unibas.ch/bfm/?nav=1-0&id=basel_face_model
• Basel Face Model 2017: http://faces.cs.unibas.ch/bfm/bfm2017.html
• Large Scale 3D Morphable Model: https://xip.uclb.com/i/healthcare_tools/LSFM.html
• Liverpool-York Head Model: https://www-users.cs.york.ac.uk/~nep/research/LYHM/
• Multilinear Autoencoder for 3D Face Model Learning: http://mae.gforge.inria.fr/
• Statistical 3D Shape Models of Human Faces: http://facepage.gforge.inria.fr/
• Surrey Face Model https://cvssp.org/facemodel
• Articulated Expressive Head Model (FLAME): http://flame.is.tue.mpg.de/
• Convolutional Mesh Autoencoder (CoMA): http://coma.is.tue.mpg.de/

Body Models:

• A skinned Multi-Person Linear Model (SMPL): http://smpl.is.tue.mpg.de/
• Expressive Body Model - SMPL with articulated hands and expressive face (SMPL-X): https://smpl-x.is.tue.mpg.de/
• MPII Human Shape Model: http://humanshape.mpi-inf.mpg.de/

Other Models:

• The York Ear Model: https://www-users.cs.york.ac.uk/~nep/research/YEM/

Registration / model construction / alignment:

• Large Scale Facial Model (LSFM) pipeline: https://github.com/menpo/lsfm
• Basel Face Registration pipeline: https://github.com/unibas-gravis/basel-face-pipeline
• Shape-aware surface reconstruction based on a statistical shape model prior: https://github.com/fbernardpi/sparsePdmFitting
• Fast Linear Assignment Problem solver: https://de.mathworks.com/matlabcentral/fileexchange/48448-fast-linear-assignment-problem-using-auction-algorithm-mex
• NmfSync for making pairwise (partial) permutations cycle-consistent (relevant for multi-matching): https://github.com/fbernardpi/NmfSync
• Transformation Synchronisation method for making pairwise transformations cycle-consistent (e.g. for solving the Generalised - - Procrustes Problem, or general multi-image registration): https://sites.google.com/site/fbernardpi/code
• Learning linear shape deformations with local support: https://sites.google.com/site/fbernardpi/code

Data / 3D scans:

Faces:

• BU-3DFE, BU-4DFE: http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html
• The Headspace dataset: https://www-users.cs.york.ac.uk/~nep/research/Headspace/
• The UoY 3D face dataset https://www-users.cs.york.ac.uk/~nep/research/UoYfaces/
• CoMA 3D face dataset (registrations): http://coma.is.tue.mpg.de/
• D3DFACS dataset: raw scan data, Registrations
• FaceCap dataset: http://gvv.mpi-inf.mpg.de/projects/FaceCap/
• MonFaceCap: http://gvv.mpi-inf.mpg.de/projects/MonFaceCap/
• Parametric Face Image Generator: https://github.com/unibas-gravis/parametric-face-image-generator
• VOCASET speech-4D head scan dataset: https://voca.is.tue.mpg.de/
• Speech-driven 3D Facial Motion Database (S3DFM): http://groups.inf.ed.ac.uk/trimbot2020/DYNAMICFACES/index.html

Bodies:

• A Model of Dynamic Human Shape in Motion: http://dyna.is.tue.mpg.de
• In the wild 3D pose dataset with ground truth: http://virtualhumans.mpi-inf.mpg.de/3DPW
• Human3.6m - 3.6 million 3D poses from 11 actors and in 17 scenarios: http://vision.imar.ro/human3.6m
• CAESAR body measurements database: https://store.sae.org/caesar/
• People in clothing scans: http://buff.is.tue.mpg.de
• Unite the People dataset: http://up.is.tuebingen.mpg.de
• Data from the GVV group: http://gvvperfcapeva.mpi-inf.mpg.de/
  • MuCo-3DHP: http://gvv.mpi-inf.mpg.de/projects/SingleShotMultiPerson/
  • MuPoTS-3D: http://gvv.mpi-inf.mpg.de/projects/SingleShotMultiPerson/
  • MonoPerfCap: http://gvv.mpi-inf.mpg.de/projects/wxu/MonoPerfCap/
  • IntrinsicMoCap: https://download.mpi-inf.mpg.de/projects/IntrinsicMoCap/
  • MPI-INF-3DHP: http://gvv.mpi-inf.mpg.de/3dhp-dataset/
  • MARCOnI: http://marconi.mpi-inf.mpg.de/
  • EgoCap: http://gvv.mpi-inf.mpg.de/projects/EgoCap/
  • MPII Human Shape: http://humanshape.mpi-inf.mpg.de/
  • BinoCap: http://gvv.mpi-inf.mpg.de/projects/BinoCap/
  • Inertial Depth Tracker Dataset: http://gvvperfcapeva.mpi-inf.mpg.de/public/InertialDepthTracker/index.php
  • Performance Capture from Sparse Multi-view Video: http://resources.mpi-inf.mpg.de/perfcap/
  • Performance Capture of Interacting Characters with Handheld Kinects: http://gvvperfcapeva.mpi-inf.mpg.de/public/KinectsMocap/

Other Data:

• 3D people in clothing from video: https://graphics.tu-bs.de/people-snapshot
• The York 3D Ear dataset: https://www-users.cs.york.ac.uk/~nep/research/YEM/
• Estimation of Human Body Shape in Motion with Wide Clothing: http://dressedhuman.gforge.inria.fr/

Model adaptation to images or videos:

Faces:

• 3D Morphable Model fitting using edge features: https://github.com/waps101/3DMM_edges
• Basic 3D Morphable Model fitting: https://github.com/anhttran/3dmm_basic
• A lightweight 3D Morphable Model fitting library: https://github.com/patrikhuber/eos
• Probabilistic Fitting: https://github.com/unibas-gravis/scalismo-faces
• Deep Neural Network parameter regression: https://talhassner.github.io/home/publication/2017_CVPR
• Expression-Net: https://github.com/fengju514/Expression-Net
• Face-Pose-Net: https://github.com/fengju514/Face-Pose-Net
• Shape-Net: https://github.com/anhttran/3dmm_cnn
• Extreme 3D Face Reconstruction: https://github.com/anhttran/extreme_3d_faces
• FLAME fitting: Chumpy-based framework, Tensorflow-based framework
• 3D face shape and expression regression network (RingNet): https://github.com/soubhiksanyal/RingNet
• Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network https://github.com/YadiraF/PRNet
• Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation https://github.com/matansel/pix2vertex
• 3D Morphable Models as Spatial Transformer Networks https://github.com/anilbas/3DMMasSTN
• Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric Regression https://github.com/AaronJackson/vrn
• Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set https://github.com/microsoft/Deep3DFaceReconstruction
• Facial Detail synthesis https://github.com/apchenstu/Facial_Details_Synthesis

Benchmarks:

• “not quite in-the-wild” (NoW) Challenge (3D face reconstruction from images benchmark): https://ringnet.is.tue.mpg.de

Bodies:

• 3D pose and shape (Neural Body Fitter): https://github.com/mohomran/neural_body_fitting
• Expressive Body Capture: 3D Hands, Face, and Body from a Single Image (SMPLify-X): https://github.com/vchoutas/smplify-x

Illumination Models:

• Basel Illumination Prior 2017: http://gravis.dmi.unibas.ch/PMM/data/bip/

Tutorials:

• Statistical Shape Modelling: https://gravis.dmi.unibas.ch/PMM/lectures/ssm/
• Probabilistic Fitting: https://gravis.dmi.unibas.ch/PMM/lectures/fitting/
• Semantic Morphable Model: https://gravis.dmi.unibas.ch/PMM/lectures/segmentation/

Web services:

• Photo-realistic Face Manipulation: https://face-morpher.scalismo.org/
• 3D Face Reconstruction from a Single Image: https://cvl-demos.cs.nott.ac.uk/vrn/