I-Scene：三維實例模型即隱式可泛化空間學習器

摘要

泛化能力仍是互動式3D場景生成的核心挑戰。現有基於學習的方法將空間理解侷限於有限場景數據集，限制了對新佈局的泛化能力。我們轉而重編程預訓練的3D實例生成器，使其成為場景級學習器，以模型為核心的空間監督取代數據集邊界的監督。這種重編程釋放了生成器的可遷移空間知識，使其能夠泛化至未見過的佈局與新穎物件組合。值得注意的是，即使訓練場景由隨機組合的物件構成，空間推理能力依然湧現。這證明生成器的可遷移場景先驗能提供豐富的學習信號，使其僅從幾何線索即可推斷鄰近性、支撐關係與對稱性。我們捨棄廣泛使用的規範空間，改以視角中心的場景空間表述來實踐此洞見，建構出完全前饋式的可泛化場景生成器，能直接從實例模型學習空間關係。量化與質化結果表明，3D實例生成器實為隱性的空間學習器與推理器，為互動式3D場景理解與生成的基礎模型指明方向。項目頁面：https://luling06.github.io/I-Scene-project/

English

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: https://luling06.github.io/I-Scene-project/

I-Scene：三維實例模型即隱式可泛化空間學習器

I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners

摘要

Support