# Node Graph Optimization Using Differentiable Proxies

arXiv: https://arxiv.org/abs/2207.07684 · HTML: https://mlsanity.com/paper/2207.07684/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2022.

## Built on by (most cited first, 13 of 13)

- [ControlMat: A Controlled Generative Approach to Material Capture](https://mlsanity.com/paper/2309.01700/) (2023)
- [PSDR-Room: Single Photo to Scene using Differentiable Rendering](https://mlsanity.com/paper/2307.03244/) (2023)
- [BlenderAlchemy: Editing 3D Graphics with Vision-Language Models](https://mlsanity.com/paper/2404.17672/) (2024)
- [Plateau-reduced Differentiable Path Tracing](https://mlsanity.com/paper/2211.17263/) (2022)
- [Improving Unsupervised Visual Program Inference with Code Rewriting Families](https://mlsanity.com/paper/2309.14972/) (2023)
- [VLMaterial: Procedural Material Generation with Large Vision-Language Models](https://mlsanity.com/paper/2501.18623/) (2025)
- [Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics](https://mlsanity.com/paper/2308.05739/) (2023)
- [TexPro: Text-guided PBR Texturing with Procedural Material Modeling](https://mlsanity.com/paper/2410.15891/) (2024)
- [Tree-Structured Shading Decomposition](https://mlsanity.com/paper/2309.07122/) (2023)
- [DreamPBR: Text-driven Generation of High-resolution SVBRDF with Multi-modal Guidance](https://mlsanity.com/paper/2404.14676/) (2024)
- [One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns](https://mlsanity.com/paper/2404.16292/) (2024)
- [MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models](https://mlsanity.com/paper/2509.22151/) (2025)
- [Pattern Analogies: Learning to Perform Programmatic Image Edits by Analogy](https://mlsanity.com/paper/2412.12463/) (2024)

## Abstract (arXiv)

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.