发表机构
ShanghaiTech University; Crysta AI（上海科技大学; Crysta人工智能公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对具象绘画的3D重建非唯一性，提出采样多视图序列并用3D高斯泼溅与DreamPrinting物化多重解，支持数字与物理形式的比较讨论。
AI 中文摘要
具象绘画常被视作描绘单一可恢复的3D场景：观众推断深度和遮挡，重建流程试图收敛到一个稳定模型。我们反而强调多重解性，即与单一绘画图像兼容的3D配置的非唯一性，并提出一种保持这种非唯一性可见且可物化的流程。多重解性源于两个来源：未观察到的内容，其中背面和被遮挡的体积允许多种合理的补全；以及观察到的线索，其中透视、阴影和遮挡仍然对几何欠约束。当视频生成模型在没有显式3D约束的情况下合成额外视图时，小的帧级漂移变得不可避免而非例外。我们的流程从一幅绘画中采样多个相机轨道多视图视频序列，使用3D高斯泼溅将每个序列重建为基于点的高斯场景表示，其中密度光晕和重影暴露未解决的自由度，并使用DreamPrinting将这些表示制作为物理人工制品。通过将多个兼容的解释视为显式输出而非残余误差，我们提供了一个计算框架，用于具象绘画的空间解读，这些解读可以在数字和物理形式中进行检查、比较和讨论。
英文摘要
Figurative paintings are often approached as if they depict a single recoverable 3D scene: viewers infer depth and occlusion, and reconstruction pipelines attempt to converge to one stable model. We instead foreground multi-solutionness, the non-uniqueness of 3D configurations compatible with a single painted image, and propose a workflow that keeps this non-uniqueness visible and material. Multi-solutionness arises from two sources: unobserved content, where backsides and occluded volumes admit multiple plausible completions, and observed cues, where perspective, shading, and occlusion still underconstrain geometry. When additional views are synthesized by a video generative model without explicit 3D constraints, small frame-level drifts become inevitable rather than exceptional. Our pipeline samples multiple camera-orbit multi-view video sequences from one painting, reconstructs each sequence with 3D Gaussian Splatting into a point-based Gaussian scene representation where density halos and ghosting expose unresolved degrees of freedom, and fabricates these representations as physical artifacts using DreamPrinting. By treating multiple compatible interpretations as explicit outputs rather than residual error, we provide a computational framework for spatial readings of figurative painting that can be inspected, compared, and discussed in both digital and physical form.
CommentsAccepted at SIGGRAPH Art Papers 2026
DOI：10.1145/3816091
