Data from: Upload any object and evolve it: injecting complex geometric patterns into CPPNs for further evolution

Ongoing, rapid advances in three-dimensional (3D) printing technology are making it inexpensive for lay people to manufacture 3D objects. However, the lack of tools to help non-technical users design interesting, complex objects represents a significant barrier preventing the public from benefitting from 3D printers. Previous work has shown that an evolutionary algorithm with a generative encoding based on developmental biology-a compositional pattern-producing network (CPPN)-can automate the design of interesting 3D shapes, but users collectively had to start each act of creation from a random object, making it difficult to evolve preconceived target shapes. In this paper, we describe how to modify that algorithm to allow the further evolution of any uploaded shape. The technical insight is to inject the distance to the surface of the object as an input to the CPPN. We show that this seeded-CPPN technique reproduces the original shape to an arbitrary resolution, yet enables morphing the shape in interesting, complex ways. This technology also raises the possibility of two new, important types of science: (1) It could work equally well for CPPN-encoded neural networks, meaning neural wiring diagrams from nature, such as the mouse or human connectome, could be injected into a neural network and further evolved via the CPPN encoding. (2) The technique could be generalized to recreate any CPPN phenotype, but substituting a flat CPPN representation for the rich, originally evolved one. Any evolvability extant in the original CPPN genome can be assessed by comparing the two, a project we take first steps toward in this paper. Overall, this paper introduces a method that will enable non-technical users to modify complex, existing 3D shapes and opens new types of scientific inquiry that can catalyze research on bio-inspired artificial intelligence and the evolvability benefits of generative encodings.当前，三维（3D）打印技术正迎来持续快速的发展，普通民众已能以较低成本制造三维物体。然而，缺乏能够帮助非专业用户设计有趣且复杂三维物体的工具，仍是阻碍公众从3D打印机中获益的一大壁垒。既往研究表明，基于发育生物学的生成式编码进化算法——即组合模式生成网络（CPPN）——可自动生成有趣的三维造型，但用户每次创作都需从随机物体起步，难以演化出预先设想的目标造型。本文提出一种对该算法的改进方案，使其能够基于任意上传的三维造型开展进一步演化。其核心技术思路是将物体表面的距离值作为输入参数注入CPPN。实验结果表明，这种带初始种子的CPPN（seeded-CPPN）技术可将原始造型以任意分辨率重现，同时还能以有趣且复杂的方式实现造型的变形。该技术还为两类全新的重要科学研究方向提供了可能：其一，该技术同样适用于CPPN编码的神经网络，这意味着可将自然界中的神经连接图谱（如小鼠或人类连接组）注入神经网络，并通过CPPN编码实现进一步演化。其二，该技术可推广至任意CPPN表型的重现，即用扁平化的CPPN表征替代原本丰富的、经演化得到的表型。通过对比这两种表征，可评估原始CPPN基因组中现存的演化能力，本文已为该研究方向迈出了初步探索的步伐。总体而言，本文提出的方法可帮助非专业用户修改现有的复杂三维造型，同时开辟了全新的科学研究方向，有望推动仿生人工智能领域的研究，以及生成式编码的演化能力优势相关探索。