ShadowDraw: From Any Object to Shadow–Drawing Compositional Art

Rundong Luo, Noah Snavely, Wei-Chiu Ma

Keywords: Computational visual art, Computation art design, Shadow art

TL;DR: We present \textsc{ShadowDraw}, a framework that transforms arbitrary 3D objects into shadow–drawing compositional art, where the cast shadow seamlessly completes a generated incomplete line drawing to form a recognizable image.

Abstract: We introduce *ShadowDraw*, a framework that transforms ordinary 3D objects into shadow–drawing compositional art. Given a 3D object, our system predicts scene parameters---including object pose and lighting---together with an incomplete line drawing, such that the cast shadow completes the drawing into a recognizable image. To this end, we optimize scene configurations to reveal meaningful shadows, employ shadow strokes to guide line drawing generation, and adopt automatic evaluation to enforce shadow-drawing coherence and visual quality. Experiments show that *ShadowDraw* produces compelling results across diverse inputs, from real-world scans and curated datasets to generative assets, and naturally extends to multi-object scenes, animations, and physical deployments. Our work provides a practical pipeline for creating shadow–drawing art and broadens the design space of computational visual art, bridging the gap between algorithmic design and artistic storytelling. Check out our anonymous [project page](https://anonymous.4open.science/w/ShadowDraw-anon-E584/) for more results.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 2164