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Learning a model of facial shape and expression from {4D} scans




The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression from 4D face sequences in the D3DFACS dataset along with additional 4D sequences.We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes (http://flame.is.tue.mpg.de).

Author(s): Tianye Li and Timo Bolkart and Michael J. Black and Hao Li and Javier Romero
Journal: ACM Transactions on Graphics
Volume: 36
Number (issue): 6
Pages: 194:1--194:17
Year: 2017
Month: November

Department(s): Perceiving Systems
Research Project(s): Faces and Expressions
Bibtex Type: Article (article)
Paper Type: Journal

Note: Two first authors contributed equally

Links: data/model
code chumpy
code tensorflow
Attachments: paper


  title = {Learning a model of facial shape and expression from {4D} scans},
  author = {Li, Tianye and Bolkart, Timo and Black, Michael J. and Li, Hao and Romero, Javier},
  journal = {ACM Transactions on Graphics},
  volume = {36},
  number = {6},
  pages = {194:1--194:17},
  month = nov,
  year = {2017},
  note = {Two first authors contributed equally},
  month_numeric = {11}