Shadow art revisited: a differentiable rendering based approach

Show simple item record Sadekar, Kaustubh Tiwari, Ashish Raman, Shanmuganathan 2012-09-29T14:45:09Z 2012-09-29T14:45:09Z 2021-07
dc.identifier.citation Sadekar, Kaustubh; Tiwari, Ashish and Raman, Shanmuganathan, "Shadow art revisited: a differentiable rendering based approach", arXiv, Cornell University Library, DOI: arXiv:2107.14539, Jul. 2021. en_US
dc.description.abstract 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.
dc.description.statementofresponsibility by Kaustubh Sadekar, Ashish Tiwari and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.title Shadow art revisited: a differentiable rendering based approach en_US
dc.type Pre-Print en_US
dc.relation.journal arXiv

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