用点云生成可搭建的积木结构,让造出来的模型既像原形又不会塌。
BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization

- 用点云统一建模,自回归预测积木序列重建目标形状。
- 提出结构感知树标记法,减少无效中间状态,提升搭建成功率。
- 适合做物理可实现积木设计的人工智能研究者与创作者。
从3D形状生成物理可搭建的积木结构,不仅需要几何重建,还需满足离散部件约束与结构稳定性。现有方法或依赖启发式优化,在特定形状下无法求解;或生成积木序列但未显式建模3D几何与组装关系。本文提出BrickAnything,一种基于点云的几何条件自回归框架,从多样3D表示中生成可搭建的积木结构。该方法以点云为统一几何接口,预测在组装约束下重构目标形状的积木序列。为建模积木间的结构依赖,引入结构感知树标记法,通过局部连接关系表示积木结构,使生成过程更符合实际搭建逻辑,降低无效中间态。进一步引入偏好对齐后训练、有效性约束解码与自适应回滚机制,提升稳定性与几何保真度。大量实验表明,BrickAnything能生成几何忠实且物理可实现的积木结构,所提标记法显著减少回滚与重生成次数,优于传统排序策略。
原文摘要 · Abstract (English)
Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic optimization, which can break down when the target 3D shape does not admit a feasible structure under predefined constraints, or generate brick sequences without explicitly modeling the underlying 3D geometry and assembly relations. In this work, we present BrickAnything, a geometry-conditioned autoregressive framework for generating buildable brick structures from diverse 3D representations. BrickAnything uses point clouds as a unified geometric interface and predicts brick sequences that reconstruct the target shape under assembly constraints. To model structural dependencies among bricks, we introduce a structure-aware tree tokenization, which represents brick structures through local attachment relations. This formulation makes sequence generation more consistent with the physical construction process, and reduces invalid intermediate states. We further introduce preference-based alignment post-training, validity-constrained decoding and adaptive rollback to improve buildability objectives such as stability and geometric fidelity. Extensive experiments demonstrate that BrickAnything produces geometrically faithful and physically realizable brick structures, and that the proposed tokenization effectively reduces rollback and regeneration compared with conventional ordering strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。