AI看3D物体，转个角度就懵了
让AI把一个3D物体转到指定角度，它连人类大学生都不如。研究者造了个测试：给AI一个物体的剪影，让它通过旋转把物体转到和剪影一致的位置。最强模型GPT-5.6-Sol成功率只有17%，而20个本科生全部完成。更糟的是，AI经常在“看起来对了”和“角度对了”之间顾此失彼——它以为自己在进步，其实越转越偏。这不是它认不出物体，而是它没法把“看到的2D画面”转化成“该转多少度”的精确指令。它不是你明天能用上的东西，但它提醒你：AI的“空间感”和人类差着一条街。
📄 原文摘要(英文)
Can vision-language models turn 2D observations into 3D understanding that supports precise action? This requires inferring object geometry, anticipating the effects of rotation, and refining actions through visual feedback. We introduce RoTA-Bench to evaluate these abilities through interactive 3D-to-2D projection alignment. Given a target silhouette, a model observes a rotated object and chooses successive rotations to recover the target projection within a limited action budget. Our automated pipeline constructs silhouette-preserving 3D objects from 2D icons and uses reversible initialization to guarantee solvability, yielding 674 tasks and a stratified 100-task test set. Empirically, we find that (i) a substantial human-model gap persists: GPT-5.6-Sol succeeds on only 17% of tasks, whereas 20 undergraduate participants collectively complete the test set four times with a 100% success rate; (ii) image-space and pose-space progress can diverge: at matched states, the model more often improves silhouette overlap while worsening pose alignment than humans (19.4% vs. 10.9%); and (iii) fine-grained control is sensitive to interaction history: removing it leaves 96.8% of the model's actions at a coarse 30-degree step size and sharply reduces observed task success. These findings highlight the challenge of converting visual evidence into precise 3D control, beyond recognizing or matching individual images.