  Differentiable rasterization changes the standard formulation of primitive
rasterization -- by enabling gradient flow from a pixel to its underlying
triangles -- using distribution functions in different stages of rendering,
creating a "soft" version of the original rasterizer. However, choosing the
optimal softening function that ensures the best performance and convergence to
a desired goal requires trial and error. Previous work has analyzed and
compared several combinations of softening. In this work, we take it a step
further and, instead of making a combinatorial choice of softening operations,
parameterize the continuous space of common softening operations. We study
meta-learning tunable softness functions over a set of inverse rendering tasks
(2D and 3D shape, pose and occlusion) so it generalizes to new and unseen
differentiable rendering tasks with optimal softness.
