clearly visible that PCA prioritises body shape over pose resulting to body parts in the wrong locations (for example see the right leg of the woman on the leftmost column). On the contrary COMA places the vertices in approximately correct locations, but struggles to recover the fine details of the shape leading to various artefacts and deformities; our model on the other hand seemingly balances these two difficult tasks resulting in quality reconstructions that preserve pose and shape.

Comparing to [@coma], it is again apparent here that our spiral-based autoencoder has increased capacity, which together with the increased parameter space, makes our larger Neural3DMM outperform the other methods by a considerably large margin in terms of both generalisation and compression. Despite the fact that for higher dimensions, PCA can explain more than 99% of the total variance, thus making it a tough-to-beat baseline, our larger model still manages to outperform it. The main advantage here is the substantially smaller number of parameters of which we make use. This is clearly seen in the comparison for the MeIn3D dataset, where the large vertex count makes non-local methods as PCA impractical. It is necessary to mention here, that larger latent space sizes 