on a pseudo-coordinate space[^2], **FeastNet** [@verma2018feastnet] and **Graph Attention** [@velivckovic2017graph], where the attention weights are learnable functions of the input features. In table \[soft\_attn\], we provide results on COMA dataset, using the simple Neural3DMM architecture with latent size 16. We choose the number of attention heads (gaussian kernels in [@monti2017geometric]) to be either 9 (equal to the size of the spiral in our method, for a fair comparison) or 25 (as in [@monti2017geometric], to showcase the effect of over-parametrisation). When it comes to similar number of parameters our method manages to outperform its counterparts, while compared to over-parametrised soft attention networks it either outperforms them, or achieves slightly worse performance. This shows that the spiral operator can make more efficient use of the available learnable parameters, thus being a lightweight alternative to attention-based methods without sacrificing performance. Also, its formulation allows for fast computation; in table \[soft\_attn\] we measure per mesh inference time in ms (on a GeForce RTX 2080 Ti GPU).

                                     Ours                       
  --------- ------- ------- ------- ------- ------- ----------- ------
  kernels      9      25       9      25       9        25        -
  error      0,762   0,732   0,750   0,623   0,708   **0,583**   ****
  params             101K             98K               95K      ****
  time       12,77   15,37   9,04    9,66    10,55     10,96     ****

  