  Graph-based procedural materials are ubiquitous in content production
industries. Procedural models allow the creation of photorealistic materials
with parametric control for flexible editing of appearance. However, designing
a specific material is a time-consuming process in terms of building a model
and fine-tuning parameters. Previous work [Hu et al. 2022; Shi et al. 2020]
introduced material graph optimization frameworks for matching target material
samples. However, these previous methods were limited to optimizing
differentiable functions in the graphs. In this paper, we propose a fully
differentiable framework which enables end-to-end gradient based optimization
of material graphs, even if some functions of the graph are non-differentiable.
We leverage the Differentiable Proxy, a differentiable approximator of a
non-differentiable black-box function. We use our framework to match structure
and appearance of an output material to a target material, through a
multi-stage differentiable optimization. Differentiable Proxies offer a more
general optimization solution to material appearance matching than previous
work.
