AI 中文总结
研究旨在解决从单幅古典绘画重建3D场景的难题，提出免训练的GEAR框架，先通过几何定位增强3D高斯重建稳定性，再在空间约束下恢复外观，经实验验证其在多方面优于基线，还构建了相关基准。
AI 中文摘要
古典绘画蕴含丰富的空间、文化和历史内容，将其重建为可探索的3D场景对数字保存、沉浸式展览和文化参与具有重要价值。然而，与照片不同，古典绘画常以单视图、风格化方式描绘场景，透视、光照和深度线索较弱。现有3D重建方法大多基于自然图像先验，难以从这类输入中恢复几何上合理且视觉上逼真的3D表示。为应对这一挑战，我们引入了古典绘画到3D（CP3D）这一新任务，旨在从单幅古典绘画中恢复3D表示，同时确保几何合理性、与源艺术品的外观保真度以及合理的新视图合成。我们进一步提出了GEAR，这是一个用于几何定位和外观还原的免训练两阶段框架。GEAR首先将输入绘画转换为具有更连贯阴影和光照线索的几何定位表示，提高3D高斯重建的稳定性。然后在空间约束和多视图一致性下恢复跨视图的艺术品忠实外观，恢复在定位过程中减弱的绘画纹理和细节。此外，我们构建了HeriArch，这是一个包含10160幅高分辨率古典艺术品的精选基准，用于系统评估CP3D。大量实验和用户研究表明，GEAR在几何合理性、外观保真度和人类偏好方面始终优于强大的基线。代码和数据集将公开发布。
英文摘要
Classical paintings preserve rich spatial, cultural, and historical content, making their reconstruction as explorable 3D scenes valuable for digital preservation, immersive exhibition, and cultural engagement. Yet, unlike photographs, they often depict scenes in a single-view, stylized manner, with weak perspective, lighting, and depth cues. Existing 3D reconstruction methods are largely built on natural-image priors, making it difficult to recover geometrically plausible and visually faithful 3D representations from such inputs. To address this challenge, we introduce Classical Painting-to-3D (CP3D), a new task that aims to recover a 3D representation from a single classical painting while jointly ensuring geometric plausibility, appearance fidelity to the source artwork, and plausible novel-view synthesis. We further propose GeAR, a training-free two-stage framework for Geometry Grounding and Appearance Restitution. GeAR first converts the input painting into a geometry-grounded representation with more coherent shading and illumination cues, improving the stability of 3D Gaussian reconstruction. It then restores artwork-faithful appearance across views under spatial constraints and multi-view consistency, recovering the painterly textures and details weakened during grounding. In addition, we construct HeriArch, a curated benchmark of 10,160 high-resolution classical artworks for systematic evaluation of CP3D. Extensive experiments and user studies show that GeAR consistently outperforms strong baselines in geometric plausibility, appearance fidelity, and human preference. Code and dataset will be released publicly.
CommentsAccepted by ACM MM 2026
