Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high-quality outputs, compact representation, and more. But they also have some limitations, such as the difficulty of authoring a procedural model from scratch. More recently, AI-based methods, and especially neural networks, have become popular for creating graphic content. These techniques allow users to directly specify desired properties of the artifact they want to create (via examples, constraints, or objectives), while a search, optimization, or learning algorithm takes care of the details. However, this ease of use comes at a cost, as it's often hard to interpret or manipulate these representations. In this state-of-the-art report, we summarize research on neurosymbolic models in computer graphics: methods that combine the strengths of both AI and symbolic programs to represent, generate, and manipulate visual data. We survey recent work applying these techniques to represent 2D shapes, 3D shapes, and materials & textures. Along the way, we situate each prior work in a unified design space for neurosymbolic models, which helps reveal underexplored areas and opportunities for future research.

翻译：程序化模型（即输出视觉数据的符号程序）是一种历史上广受欢迎的图形内容表示方法，涵盖植被、建筑、纹理等领域。这类模型具有诸多优势：可解释的设计参数、随机变体生成、高质量输出、紧凑表示能力等。然而，它们也存在局限性，例如从零构建程序化模型的困难性。近年来，基于人工智能的方法，特别是神经网络，逐渐成为图形内容创作的主流技术。这些技术允许用户直接指定期望创建的产物特性（通过示例、约束或目标），而搜索、优化或学习算法则负责处理具体细节。但这种易用性是有代价的——这些表示往往难以解释或操控。在本前沿进展报告中，我们系统梳理了计算机图形学中神经符号模型的研究：这类方法融合了人工智能与符号程序的优势，用于表示、生成和操控视觉数据。我们重点调研了近年来将此类技术应用于二维形状、三维形状、材质及纹理表示的研究进展。在综述过程中，我们将每项前期工作置于统一的神经符号模型设计空间中，这有助于揭示尚未充分探索的领域和未来研究机遇。