ShadowDraw: From Any Object to Shadow-Drawing Compositional Art
Luo, Rundong, Snavely, Noah, Ma, Wei-Chiu
–arXiv.org Artificial Intelligence
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!
arXiv.org Artificial Intelligence
Dec-5-2025
- Genre:
- Research Report (0.50)
- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning > Neural Networks (0.94)
- Natural Language (1.00)
- Representation & Reasoning (0.67)
- Vision (1.00)
- Graphics (0.88)
- Artificial Intelligence
- Information Technology