# Arbor：显式几何约束实现可控3D资产生成

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-22 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmqqn59r20bjvslp5u5hfsz4z
- 原文链接：https://arxiv.org/abs/2606.23514

## AI 摘要

Arbor是一个可训练的附件，为文本条件潜空间3D生成引入约束网格作为原生3D控制接口。约束分三类：包络区域（应有几何）、避让区域（保持空白）和接触区域（物体应接触）。Arbor将约束网格转换为模型token，在冻结去噪器内学习路由附件，使每个潜空间区域只接收相关约束信号。在自动和艺术家控制基准测试中，Arbor在固定约束下提升了约束遵循度，同时保持了对象质量和多样性。

## 正文

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.