Arbor: 面向可控3D资产生成的显式几何条件约束

摘要

文本与图像条件化的3D模型如今已能生成令人信服的资产，但在物体应占据或回避的空间控制上仍缺乏直接手段。在创作过程中，这类空间意图往往在生成开始前就已明确：椅子需适配坐姿包络空间，道具应为动作预留间隙，部件应暴露出接触表面。提示词与图像视角难以有效承载这类约束，因此亟需显式控制接口。我们提出Arbor——一种用于文本条件化潜在3D生成的可训练附加模块。Arbor将约束网格作为原生3D控制接口引入，该接口包含三类区域：几何体应存在的包络区域、应保持空旷的回避区域、以及物体需接触的接触区域。与补全或整体物体骨架控制不同，这些网格并非目标证据，而是局部化类型化约束，甚至可包含不应出现表面的区域。Arbor将约束信号保留为几何形态，通过将约束网格转化为隐向量标记，并在冻结去噪器内部学习路由附加机制，使每个潜在区域都能接收与其空间位置相关的约束部分。我们在包含包络、回避与接触约束的自动化及艺术家策划控制基准上评估Arbor，并将指标趋势与用户偏好研究进行对比。即便未设置专门的合规性损失，Arbor仍能在固定约束下提升约束遵守度，同时保持物体质量与多样性。

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.