Arbor：用于可控3D资源生成的显式几何约束方法
Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation
摘要
文本和图像所控制的3D模型能够生成令人信服的虚拟对象，但它们在对物体应占据或避免的空间范围方面仍缺乏直接的控制能力。在创作过程中，这种空间约束通常在生成过程开始之前就已知晓。例如，椅子应该适合特定的座位区域；道具则应该留出足够的空间以允许物体移动；而某个部件则应该具有可接触的表面。对于这类约束条件来说，提示词和图像描述方式并不适合用于表达这些约束条件，因此需要一种明确的控制界面来实施这些约束。 我们提出了Arbor这一工具，它是一种可用于文本控制的潜变量3D生成工具。Arbor将约束网格作为原生3D控制界面来使用。该界面利用“ hull区域”来表示物体应该存在的几何形状，“避免区域”则表示那些应该保持为空的区域，而“接触区域”则表示物体应该与之接触的部分。与传统的完整物体控制方式不同，这些网格并非基于目标信息而生成的，而是基于局部约束条件来确定的。它们可以包含那些不应该有表面的区域。Arbor通过将约束网格转化为具体的标记，并在冻结的去噪过程中进行学习，从而将这种约束信息转化为实际的几何形状。因此，每个潜变量区域都可以获得与其空间位置相关的约束信息。 我们在自动控制和人工设计的测试环境中对Arbor进行了评估，这些测试涉及hull、避免和接触等约束条件。我们将这些指标与用户偏好研究的结果进行了比较。即使没有专门的合规损失情况，Arbor也能提高对约束条件的遵守程度，同时又能保持物体的质量和多样性，尤其是在固定的约束条件下。
English Abstract
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.