representations than the linear 3DMM and COMA, while maintaining a small parameter count and frequently allowing a more compact latent representation. Moreover, we proceed with a qualitative evaluation of our method by generating novel examples through vector space arithmetic. Finally, we assess our intrinsic GAN in terms of its ability to produce high resolution realistic examples.

For all the cases, we choose as signal on the mesh the normalised deformations from the mean shape, for every vertex we subtract its mean position and divide with the standard deviation. In this way, we encourage signal stationarity, thus facilitating optimisation. The code is available at <https://github.com/gbouritsas/neural3DMM>.

Datasets
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**COMA.** The facial expression dataset from Ranjan [@coma], consisting of 20K+ 3D scans (5023 vertices) of twelve unique identities performing twelve types of extreme facial expressions. We used the same data split as in [@coma]. **DFAUST.** The dynamic human body shape dataset from Bogo [@dfaust:CVPR:2017], consisting of 40K+ 3D scans (6890 vertices) of ten unique identities performing actions such as leg and arm raises, jumps, etc. We randomly split the data into a test set of 5000, 500 validation, and 34,5K+ train.

**MeIn3D.** The 3D large scale facial identity dataset from Booth [@booth20163d], consisting 