While there have been previous works that explored methods to enhance the aesthetics of images, the automated beautification of 3D shapes has been limited to specific shapes such as 3D face models. In this paper, we introduce a framework to automatically enhance the aesthetics of general 3D shapes. Our approach employs a reference-based beautification strategy. We first performed data collection to gather the aesthetics ratings of various 3D shapes to create a 3D shape aesthetics dataset. Then we perform reference-based editing to edit the input shape and beautify it by making it look more like some reference shape that is aesthetic. Specifically, we propose a reference-guided global deformation framework to coherently deform the input shape such that its structural proportions will be closer to those of the reference shape. We then optionally transplant some local aesthetic parts from the reference to the input to obtain the beautified output shapes. Comparisons show that our reference-guided 3D deformation algorithm outperforms existing techniques. Furthermore, quantitative and qualitative evaluations demonstrate that the performance of our aesthetics enhancement framework is consistent with both human perception and existing 3D shape aesthetics assessment.

中文翻译：

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通过基于参考的编辑增强 3D 形状的美感


虽然之前有作品探索了增强图像美感的方法，但 3D 形状的自动美化仅限于特定形状，例如 3D 面部模型。在本文中，我们介绍了一个框架，可以自动增强一般 3D 形状的美感。我们的方法采用基于参考的美化策略。我们首先进行数据收集，以收集各种 3D 形状的美学评级，以创建 3D 形状美学数据集。然后，我们执行基于引用的编辑来编辑输入形状，并通过使其看起来更像一些美观的参考形状来美化它。具体来说，我们提出了一个参考导向的全局变形框架，以连贯地变形输入形状，使其结构比例更接近参考形状的比例。然后，我们可以选择从引用中移植一些局部美学部分到输入，以获得美化后的输出形状。比较表明，我们的参考引导 3D 变形算法优于现有技术。此外，定量和定性评估表明，我们的美学增强框架的性能与人类感知和现有的 3D 形状美学评估一致。