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@ -485,7 +485,7 @@ _Parametric Morphable Model-based methods_
<b>A Morphable Model For The Synthesis Of 3D Faces (1999)</b> [[Paper]](http://gravis.dmi.unibas.ch/publications/Sigg99/morphmod2.pdf)[[Code]](https://github.com/MichaelMure/3DMM) <b>A Morphable Model For The Synthesis Of 3D Faces (1999)</b> [[Paper]](http://gravis.dmi.unibas.ch/publications/Sigg99/morphmod2.pdf)[[Code]](https://github.com/MichaelMure/3DMM)
<p align="center"><img width="40%" src="http://mblogthumb3.phinf.naver.net/MjAxNzAzMTdfMjcz/MDAxNDg5NzE3MzU0ODI3.9lQioLxwoGmtoIVXX9sbVOzhezoqgKMKiTovBnbUFN0g.sXN5tG4Kohgk7OJEtPnux-mv7OAoXVxxCyo3SGZMc6Yg.PNG.atelierjpro/031717_0222_DataDrivenS4.png?type=w420" /></p> <p align="center"><img width="40%" src="http://mblogthumb3.phinf.naver.net/MjAxNzAzMTdfMjcz/MDAxNDg5NzE3MzU0ODI3.9lQioLxwoGmtoIVXX9sbVOzhezoqgKMKiTovBnbUFN0g.sXN5tG4Kohgk7OJEtPnux-mv7OAoXVxxCyo3SGZMc6Yg.PNG.atelierjpro/031717_0222_DataDrivenS4.png?type=w420" /></p>
<b>FLAME: Faces Learned with an Articulated Model and Expressions (2017)</b> [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/400/paper.pdf)[[Code (Chumpy)]](https://github.com/Rubikplayer/flame-fitting)[[Code (TF)]](https://github.com/TimoBolkart/TF_FLAME) <b>FLAME: Faces Learned with an Articulated Model and Expressions (2017)</b> [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/400/paper.pdf)[[Code (Chumpy)]](https://github.com/Rubikplayer/flame-fitting)[[Code (TF)]](https://github.com/TimoBolkart/TF_FLAME) [[Code (PyTorch)]](https://github.com/HavenFeng/photometric_optimization)
<br>[FLAME](http://flame.is.tue.mpg.de/) is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. The model combines a linear identity shape space (trained from 3800 scans of human heads) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. <br>[FLAME](http://flame.is.tue.mpg.de/) is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. The model combines a linear identity shape space (trained from 3800 scans of human heads) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes.
The code demonstrates how to 1) reconstruct textured 3D faces from images, 2) fit the model to 3D landmarks or registered 3D meshes, or 3) generate 3D face templates for [speech-driven facial animation](https://github.com/TimoBolkart/voca). The code demonstrates how to 1) reconstruct textured 3D faces from images, 2) fit the model to 3D landmarks or registered 3D meshes, or 3) generate 3D face templates for [speech-driven facial animation](https://github.com/TimoBolkart/voca).
<p align="center"> <img width="50%" src="https://github.com/TimoBolkart/TF_FLAME/blob/master/gifs/model_variations.gif"></p> <p align="center"> <img width="50%" src="https://github.com/TimoBolkart/TF_FLAME/blob/master/gifs/model_variations.gif"></p>
@ -496,6 +496,9 @@ The code demonstrates how to 1) reconstruct textured 3D faces from images, 2) fi
<b>SMPL-X: Expressive Body Capture: 3D Hands, Face, and Body from a Single Image (2019)</b> [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/497/SMPL-X.pdf)[[Video]](https://youtu.be/XyXIEmapWkw)[[Code]](https://github.com/vchoutas/smplify-x) <b>SMPL-X: Expressive Body Capture: 3D Hands, Face, and Body from a Single Image (2019)</b> [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/497/SMPL-X.pdf)[[Video]](https://youtu.be/XyXIEmapWkw)[[Code]](https://github.com/vchoutas/smplify-x)
<p align="center"> <img width="50%" src="https://github.com/vchoutas/smplify-x/blob/master/images/teaser_fig.png"></p> <p align="center"> <img width="50%" src="https://github.com/vchoutas/smplify-x/blob/master/images/teaser_fig.png"></p>
<b>ExPose: Monocular Expressive Body Regression through Body-Driven Attention (2020)</b> [[Paper]](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/620/0983.pdf)[[Video]](https://youtu.be/lNTmHLYTiB8)[[Code]](https://github.com/vchoutas/expose)
<p align="center"> <img width="50%" src="https://github.com/vchoutas/expose/blob/master/images/expose.png"></p>
<b>Category-Specific Object Reconstruction from a Single Image (2014)</b> [[Paper]](https://people.eecs.berkeley.edu/~akar/categoryshapes.pdf) <b>Category-Specific Object Reconstruction from a Single Image (2014)</b> [[Paper]](https://people.eecs.berkeley.edu/~akar/categoryshapes.pdf)
<p align="center"><img width="50%" src="http://people.eecs.berkeley.edu/~akar/categoryShapes/images/teaser.png" /></p> <p align="center"><img width="50%" src="http://people.eecs.berkeley.edu/~akar/categoryShapes/images/teaser.png" /></p>