ShadowDraw: From Any Object to Shadow-Drawing Compositional Art
This work provides a practical pipeline for creating shadow-drawing art, broadening the design space of computational visual art for artists and designers, though it appears incremental as it builds on existing methods for scene optimization and line drawing generation.
The paper tackles the problem of transforming 3D objects into shadow-drawing compositional art by predicting scene parameters and generating partial line drawings, with results shown to be compelling across diverse inputs like real-world scans and generative assets.
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 a partial 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 project page https://red-fairy.github.io/ShadowDraw/ for more results and an end-to-end real-world demonstration of our pipeline!