Arbor: 用於可控3D資產生成的顯式幾何條件化

摘要

文字與圖像條件之3D模型現已能生成令人信服的資產，但仍難以直接控制物體應佔據或避開的空間。在創作過程中，此類空間意圖通常在生成開始前便已明確：椅子需符合乘坐範圍、道具需預留動作間隙、零件需露出接觸面。提示詞與圖像視角難以有效承載此類約束，凸顯出明確控制介面的必要性。我們提出 Arbor，一種可附加於文字條件式潛在3D生成的訓練模組。Arbor 引入約束網格作為原生3D控制介面，此介面包含三種區域：物體應存在的「外殼區域」、需保持空曠的「迴避區域」，以及物體需觸及的「接觸區域」。不同於補全或整體物體骨架控制，這些網格並非目標證據，而是局部型態的要求，甚至可包含不應出現表面的區域。Arbor 將約束網格轉換為語意標記，並在凍結去噪器中學習路由附加機制，使此信號以幾何形式保留。因此，每個潛在區域僅接收與其空間位置相關的約束部分。我們在外殼、迴避與接觸約束條件下，對 Arbor 進行自動化與藝術家策劃之控制基準測試，並將指標趨勢與用戶偏好研究進行比較。即便無專用合規損失函數，Arbor 仍能在固定約束下提升約束遵從度，同時維持物件品質與生成多樣性。

English

Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy or avoid. In authoring, this spatial intent is often known before generation starts. A chair should fit a seating envelope, a prop should leave clearance for motion, or a part should expose a contact surface. Prompts and image views are poor carriers for such constraints, requiring the need for an explicit control interface. We present Arbor, a trainable attachment for text conditioned latent 3D generation. Arbor introduces constraint meshes as a native 3D control interface. The interface uses hull regions where geometry should exist, avoidance regions that should remain empty, and touch regions the object should contact. Unlike completion or whole object scaffold control, these meshes are not target evidence. They are local typed requirements and can include regions where no surface should appear. Arbor keeps this signal as geometry by converting constraint meshes into tokens and learning a routed attachment inside a frozen denoiser. Each latent region can therefore receive the part of the constraint that matters for its spatial location. We evaluate Arbor on automatic and artist curated control benchmarks with hull, avoidance, and touch constraints, and compare the metric trends to a user preference study. Even without dedicated compliance losses, Arbor improves constraint obedience while preserving object quality and variation under fixed constraints.