While recent learning based methods have been observed to be superior for several vision-related applications, their potential in generating artistic effects has not been explored much. One such interesting application is Shadow Art - a unique form of sculptural art where 2D shadows cast by a 3D sculpture produce artistic effects. In this work, we revisit shadow art using differentiable rendering based optimization frameworks to obtain the 3D sculpture from a set of shadow (binary) images and their corresponding projection information. Specifically, we discuss shape optimization through voxel as well as mesh-based differentiable renderers. Our choice of using differentiable rendering for generating shadow art sculptures can be attributed to its ability to learn the underlying 3D geometry solely from image data, thus reducing the dependence on 3D ground truth. The qualitative and quantitative results demonstrate the potential of the proposed framework in generating complex 3D sculptures that go beyond those seen in contemporary art pieces using just a set of shadow images as input. Further, we demonstrate the generation of 3D sculptures to cast shadows of faces, animated movie characters, and applicability of the framework to sketch-based 3D reconstruction of underlying shapes.

中文翻译：

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重新审视阴影艺术：一种基于可微分渲染的方法

虽然最近的基于学习的方法已被观察到在几个与视觉相关的应用中表现更好，但它们在产生艺术效果方面的潜力尚未得到太多探索。一个这样有趣的应用程序是阴影艺术 - 一种独特的雕塑艺术形式，其中 3D 雕塑投射的 2D 阴影产生艺术效果。在这项工作中，我们使用基于可微渲染的优化框架重新审视阴影艺术，从一组阴影（二进制）图像及其相应的投影信息中获取 3D 雕塑。具体来说，我们通过体素和基于网格的可微渲染器讨论形状优化。我们选择使用可微渲染来生成阴影艺术雕塑可以归因于它能够仅从图像数据中学习底层 3D 几何，从而减少对 3D 地面实况的依赖。定性和定量结果证明了所提出的框架在生成复杂的 3D 雕塑方面的潜力，这些雕塑超越了仅使用一组阴影图像作为输入的当代艺术作品中所见。此外，我们展示了生成 3D 雕塑以投射面部阴影、动画电影角色，以及该框架对基于草图的底层形状 3D 重建的适用性。