Wu, ChenghaoMailee, HamilaMontazeri, ZahraRitschel, TobiasGarces, ElenaHaines, Eric2024-06-252024-06-2520241467-8659https://doi.org/10.1111/cgf.15145https://diglib.eg.org/handle/10.1111/cgf15145Differentiable 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.CCS Concepts: Computing methodologies → Rendering; Rasterization; Artificial intelligenceComputing methodologies → RenderingRasterizationArtificial intelligence10.1111/cgf.1514511 pages