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.

This list also accompanies our survey on 3D Morphable models:

3D Morphable Face Models - Past, Present and Future
Bernhard Egger, William A.P Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, Christian Theobalt, Volker Blanz, Thomas Vetter
ACM Transactions on Graphics, 2020
DOI: https://doi.org/10.1145/3395208
https://arxiv.org/abs/1909.01815

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
• FLAME: Articulated Expressive Head Model: http://flame.is.tue.mpg.de/
• CoMA: Convolutional Mesh Autoencoder: http://coma.is.tue.mpg.de/
• ICT-FaceKIT - ICT Face Model: https://github.com/VGL-Group/ICT-FaceKit
• Albedo Morphable Model: https://github.com/waps101/AlbedoMM
• FaceVerse: https://www.liuyebin.com/faceverse/faceverse.html
• INFACE: https://github.com/tillns/INFACE

Body Models:

• SMPL: A skinned Multi-Person Linear Model: http://smpl.is.tue.mpg.de/
• SMPL-X: Expressive Body Model - SMPL with articulated hands and expressive face: https://smpl-x.is.tue.mpg.de/
• MPII Human Shape Model: http://humanshape.mpi-inf.mpg.de/
• GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models: https://github.com/google-research/google-research/tree/master/ghum
• STAR: Sparse Articulated Human Body Model: http://star.is.tue.mpg.de/

Other Models:

• The York Ear Model: https://www-users.cs.york.ac.uk/~nep/research/YEM/
• SMAL A Skinned Multi-Animal Linear Model of 3D Animal Shape http://smal.is.tue.mpg.de/
• CAFM: A 3D Morphable Model for Animals https://github.com/sunyifan2017/CAFM-A-3D-Morphable-Model-for-Animals
• CAPE: Clothed Auto Person Encoding https://cape.is.tue.mpg.de/

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
• 3D Face Modeling from Diverse Raw Scan Data: https://github.com/liuf1990/3DFC
• 3D Growth Curves https://github.com/harrymatthews50/3DGrowthCurves
• Registration by Surface-to-Surface Translation https://github.com/mbahri/smf

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 (scans, 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
• The Florence 2D/3D hybrid face dataset: http://www.micc.unifi.it/vim/3dfaces-dataset/
• Parametric Mooney Face Generator: https://github.com/updown2/MooneyFaceGenerator
• FaceScape 3D dataset https://facescape.nju.edu.cn/
• H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction https://crisalixsa.github.io/h3d-net/
• Towards a complete 3D morphable model of the human head https://github.com/steliosploumpis/Universal_Head_3DMM
• Multiface: https://github.com/facebookresearch/multiface

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
• CMU Panoptic dataset: http://domedb.perception.cs.cmu.edu/dataset.html
• Human Behavioral Multiview Imaging Dataset (HUMBI): https://humbi-data.net/
• 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/
• YouTube 3D Hands (YT 3D): https://github.com/arielai/youtube_3d_hands

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
• face3d: Python tools for processing 3D face https://github.com/YadiraF/face3d
• Photometric FLAME Fitting: https://github.com/HavenFeng/photometric_optimization
• Single Shot Multiple Face Reconstruction: https://github.com/kalyo-zjl/WM3DR
• Rotate and Render: https://github.com/Hangz-nju-cuhk/Rotate-and-Render
• DECA: Detailed Expression Capture and Animation: https://github.com/YadiraF/DECA
• Nonlinear 3D Face Morphable Model: https://github.com/tranluan/Nonlinear_Face_3DMM
• Face Alignment in Full Pose Range: A 3D Total Solution: https://github.com/cleardusk/3DDFA
• Towards Fast, Accurate and Stable 3D Dense Face Alignment: https://github.com/cleardusk/3DDFA_V2
• PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering: https://github.com/RenYurui/PIRender
• Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry: https://github.com/choyingw/SynergyNet
• VariTex: Variational Neural Face Textures: https://github.com/mcbuehler/VariTex
• Facial Geometric Detail Recovery via Implicit Representation: https://github.com/deepinsight/insightface/tree/master/reconstruction/PBIDR
• To fit or not to fit: Occlusion Robust MoFA: https://github.com/unibas-gravis/Occlusion-Robust-MoFA

Other

• Three-D Safari https://github.com/silviazuffi/smalst

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
• SMPLify-X: Expressive Body Capture: 3D Hands, Face, and Body from a Single Image: https://github.com/vchoutas/smplify-x
• ExPose: Monocular Expressive Body Regression through Body-Driven Attention: https://github.com/vchoutas/expose

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/
• Pix2vertex : https://mybinder.org/v2/gh/eladrich/pix2vertex.pytorch/mybinder

To be released: