ShadowDraw：从任意物体到阴影绘画组合艺术
ShadowDraw: From Any Object to Shadow-Drawing Compositional Art
将3D物体投影为阴影并生成互补线稿，为计算视觉艺术提供可复用的场景优化与轮廓引导生成管线，可启发基于物理投影的图像合成与编辑。
原文依据 we optimize scene configurations to reveal meaningful shadows, employ shadow contour to guide line drawing generation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 ShadowDraw，一个将普通 3D 物体转化为阴影绘画组合艺术的框架。给定一个 3D 物体，我们的系统预测场景参数——包括物体姿态和光照——以及部分线稿，使得投射的阴影将绘画补全为可识别的图像。为此，我们优化场景配置以显现有意义的阴影，采用阴影轮廓引导线稿生成，并采用自动评估来强制阴影与绘画的连贯性和视觉质量。实验表明，ShadowDraw 在多样输入上产生引人注目的结果，从真实世界扫描和精选数据集到生成资产，并自然扩展到多物体场景、动画和物理部署。我们的工作为创建阴影绘画艺术提供了实用流程，拓宽了计算视觉艺术的设计空间，弥合了算法设计与艺术叙事之间的差距。
英文原文摘要 972 字符
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 contour 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.