# Shadow Art Revisited: A Differentiable Rendering Based Approach

arXiv: https://arxiv.org/abs/2107.14539 · HTML: https://mlsanity.com/paper/2107.14539/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency](https://mlsanity.com/paper/1704.06254/) (2017)
- [Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids](https://mlsanity.com/paper/1904.09970/) (2019)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [Learning to Infer Implicit Surfaces without 3D Supervision](https://mlsanity.com/paper/1911.00767/) (2019)
- [RenderNet: A deep convolutional network for differentiable rendering from 3D shapes](https://mlsanity.com/paper/1806.06575/) (2018)

## Built on by (most cited first, 10 of 10)

- [Shadows Shed Light on 3D Objects](https://mlsanity.com/paper/2206.08990/) (2022)
- [Illusion3D: 3D Multiview Illusion with 2D Diffusion Priors](https://mlsanity.com/paper/2412.09625/) (2024)
- [Differentiable Shadow Mapping for Efficient Inverse Graphics](https://mlsanity.com/paper/2308.10896/) (2023)
- [Hand-Shadow Poser](https://mlsanity.com/paper/2505.07012/) (2025)
- [RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects](https://mlsanity.com/paper/2504.02465/) (2025)
- [Neural Shadow Art](https://mlsanity.com/paper/2411.19161/) (2024)
- [Shape from Semantics: 3D Shape Generation from Multi-View Semantics](https://mlsanity.com/paper/2502.00360/) (2025)
- [ShadowDraw: From Any Object to Shadow-Drawing Compositional Art](https://mlsanity.com/paper/2512.05110/) (2025)
- [JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoising](https://mlsanity.com/paper/2606.20563/) (2026)
- [Mirror Illusion Art](https://mlsanity.com/paper/2607.02015/) (2026)

## Abstract (arXiv)

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.