ShadowDraw: From Any Object to Shadow-Drawing Compositional Art

TL;DR

ShadowDraw introduces a unified pipeline that turns arbitrary 3D objects into shadow-drawing art by jointly optimizing scene parameters and a shadow-contour conditioned line-drawing generator. The method leverages differentiable rendering to explore semantically meaningful shadows, uses shadow contours for stronger conditioning and scalable data, and deploys an automatic, multi-faceted evaluation and ranking framework to filter high-quality results. It demonstrates strong results across diverse assets, supports multi-object scenes, animation, and real-world deployment with minimal hardware. By bridging physical shadow phenomena with generative drawing and vision–language guidance, ShadowDraw broadens the design space for computational visual art and enables accessible, storytelling-driven creation.

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 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!

Figures (12)

• Figure 1: Generating shadow–drawing compositional art. Given an arbitrary 3D object, our framework jointly predicts scene parameters, including object pose and lighting, and generates a partial line drawing, such that the cast shadow seamlessly completes the drawing into a coherent image. The system unites physical shadows with generative drawing, creating compelling compositions from cast shadows that provide only minimal structural cues. Our approach enables straightforward real-world deployment, as demonstrated with physical prototypes of letters C, V, P, R. Best viewed in Adobe Acrobat Reader for the embedded animation.
• Figure 2: From traditional shadow art to shadow–drawing compositional art. (a) Traditional shadow art, such as artist-crafted works and computational art designs, treats the objects and their cast shadows as the sole medium. (b) Our framework integrates shadows with line drawings: given a 3D object, we generate a partial drawing and estimate scene parameters (e.g., object pose and light position) such that the cast shadow completes the composition.
• Figure 3: Framework overview. Given a 3D object, we first optimize scene parameters specifying the object pose and light configuration. From the rendered shadows, we derive text prompts with VLM and extract shadow contours, which together condition the line drawing generator. The generated drawings are then filtered using a VQA-based coherence check and ranked by semantic and quality metrics. The final output is a partial line drawing along with scene parameters that, when rendered, form a coherent shadow–drawing composition.
• Figure 4: Examples of objects for evaluation.
• Figure 5: Qualitative baseline comparisons. Large models like Gemini fail to capture the subtle notion of shadow–drawing art and often produce outputs where the shadow contributes little, whereas our method yields coherent shadow–drawing compositions of better quality.