把照片变成可编辑的3D程序，AI做到了
一张照片重建3D场景不稀奇，稀奇的是重建结果不是一张图或一个模型，而是一段能改、能查、能重新运行的代码。LEGO-Anything让AI像程序员一样反复写Blender代码、渲染检查、再修改，直到场景像样。它带来的新评测显示，最强模型在室内外场景的得分也只有五成上下，离忠实还原还差得远。但研究者发现，把重建结果变成程序后，你可以在上面直接问“图里有几个物体”“它们的轮廓和深度是多少”，AI能答出来，虽然不如专门的视觉模型准。这意味着未来你拍一张照片，得到的可能不是一个死图片，而是一个能拆开、能调整、能查询的虚拟场景——比如把照片里的沙发换个颜色、把窗户挪个位置，或者直接问“这个房间的灯在哪”。它还不是你明天就能用的工具，但方向很明确：3D重建正在从“生成画面”走向“生成可编辑的世界”。
📄 原文摘要(英文)
A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program whose execution yields a scene that can be inspected, edited, and queried. We present LEGO-Anything, an Image-to-Code framework in which a coding agent iteratively writes and executes Blender code, inspects scenes and renderings, and revises the program. To evaluate end-to-end scene recovery, we introduce LEGO-Bench, a simulator-grounded benchmark with 208 images from 104 diverse indoor and outdoor scenes. LEGO-Bench separately scores artifact validity, visible-surface geometry, and rendered appearance. Its simulator-grounded design enables extensibility and precise automatic evaluation. Among evaluated agents, GPT-6-astra achieves the strongest overall results, with 53.4% indoor and 39.6% outdoor scores, yet substantial gaps remain between delivering valid scene artifacts and faithfully recovering scene geometry and appearance. Analysis of agent construction trajectories reveals three recurring issues: weak scene initialization, regressive edits during iteration, and unreliable self-evaluation. These findings motivate LEGO-Plugin, a training-free harness plugin for more controlled iterative scene construction, which improves all six evaluated models, with relative gains of up to 62.7% in overall score. Finally, we test whether reconstructed scenes can represent natural images and support vision tasks. In LEGO-World, we derive object detections, instance masks, and relative depth as deterministic queries on scenes reconstructed by GPT-6-astra. These readouts show non-trivial performance across all three tasks but fall well short of specialized vision models, suggesting that program-constructed scenes from current coding agents are a promising but not yet sufficiently precise representation of natural images.