row is ground truth.[]{data-label="fig:dfaust_color_coding"}](dfaust_color_coding.jpg){width="0.6\linewidth"}

### Quantitative results

In this section, we compare the following methods for different dimensions of the latent space: **PCA**, the 3D Morphable Model [@vetterMM], **COMA**, the ChebNet-based Mesh Autoencoder, **Neural3DMM (small)**, ours spiral convolution autoencoder with the same architecture as in COMA, **Neural3DMM (ours)**, our proposed Neural3DMM framework, where we enhanced our model with a larger parameter space (see Sec. \[implementation\]). The latent sizes were chosen based on the variance explained by PCA (explained variance of roughly 85%, 95% and 99% of the total variance).

As can be seen from the graphs in Fig \[quantitative\], our Neural3DMM achieves smaller generalisation errors in every case it was tested on. For the COMA and DFAUST datasets all hierarchical intrinsic architectures outperform PCA for small latent sizes. That should probably be attributed to the fact that the localised filters used allow for effective reconstruction of smaller patches of the shape, such as arms and legs (for the DFAUST case), whilst PCA attempts a more global reconstruction, thus its error is distributed equally across the entire shape. This is well shown in Fig \[fig:dfaust\_color\_coding\], where we compare exemplar reconstructions of samples from the test set (latent size 16). It is 