Abstract
Generalization remains the central challenge for interactive 3D scene generation. Existing learning-based approaches ground spatial understanding in limited scene dataset, restricting generalization to new layouts. We instead reprogram a pre-trained 3D instance generator to act as a scene level learner, replacing dataset-bounded supervision with model-centric spatial supervision. This reprogramming unlocks the generator transferable spatial knowledge, enabling generalization to unseen layouts and novel object compositions. Remarkably, spatial reasoning still emerges even when the training scenes are randomly composed objects. This demonstrates that the generator's transferable scene prior provides a rich learning signal for inferring proximity, support, and symmetry from purely geometric cues. Replacing widely used canonical space, we instantiate this insight with a view-centric formulation of the scene space, yielding a fully feed-forward, generalizable scene generator that learns spatial relations directly from the instance model. Quantitative and qualitative results show that a 3D instance generator is an implicit spatial learner and reasoner, pointing toward foundation models for interactive 3D scene understanding and generation. Project page: this https URL
Abstract (translated)
泛化仍然是互动式三维场景生成的核心挑战。现有的基于学习的方法将空间理解建立在有限的场景数据集上，从而限制了其对新布局的一般化能力。我们则重新编程了一个预训练的3D实例生成器，使其作为场景级的学习者来运作，并用模型中心的空间监督取代了局限于数据集的监督。这种重编程解锁了生成器可迁移的空间知识，使它能够推广到未见过的布局和新物体组合上。值得注意的是，即使在由随机组合的对象构成的训练场景中，空间推理仍然出现。这表明生成器的可转移场景先验提供了丰富的学习信号，可以从纯粹的几何线索中推断出接近性、支撑性和对称性。 通过用以视角为中心的形式化替代广泛使用的标准空间概念，我们实现了这一洞察，并提出了一种完全前馈的、通用化的场景生成器，该生成器直接从实例模型中学得空间关系。定量和定性的结果表明，一个3D实例生成器是一个隐含的空间学习者和推理工具，这指向了用于交互式3D场景理解和生成的基础模型的发展方向。 项目页面: [此链接](this https URL)
URL
https://arxiv.org/abs/2512.13683
