We introduce Kinematic Kitbashing, an optimization framework that synthesizes articulated 3D objects by assembling reusable parts conditioned on an abstract kinematic graph. Given the graph and a library of articulated parts, our method optimizes per-part similarity transformations that place, orient, and scale each component into a coherent articulated object; optional graph edits further enable novel assemblies beyond the prescribed connectivity. Central to our method is an exemplar-based analogy for part placement: each reused component is paired with a single source asset that exemplifies how it attaches to its parent. We capture this attachment context using vector distance fields and measure consistency by integrating the matching error over the joint's full motion range. This yields a kinematics-aware attachment energy that favors placements that preserve the exemplar's local attachment neighborhood throughout articulation. To incorporate task-level functionality, we use this attachment energy as a prior in an annealed Langevin sampling framework, enabling gradient-free optimization of black-box functionality objectives. We demonstrate the versatility of kinematic kitbashing across diverse applications, including instantiating kinematic graphs from user-selected or automatically retrieved parts, synthesizing assemblies with user-defined functionality, and re-targeting articulations via graph edits.

翻译：我们提出运动学组件拼装（Kinematic Kitbashing），这是一种通过组装可复用部件来合成铰接式三维物体的优化框架，该框架以抽象运动学图作为约束条件。给定运动学图与铰接部件库后，本方法可优化每个部件的相似变换参数（包括位置、朝向与缩放比例），从而将这些部件组合成连贯的铰接式物体；可选的图编辑功能还允许在预设连接关系之外生成新型装配体。本方法的核心在于基于范例的类比式部件定位：每个复用部件与单一源资产配对，该源资产展示了该部件如何连接至其父部件。我们利用向量距离场捕获这种连接上下文，并通过将匹配误差沿关节完整运动范围进行积分来度量一致性。由此产生的运动学感知连接能量函数倾向于保留范例在关节运动过程中局部连接邻域特性的放置方案。为融入任务级功能性，我们将该连接能量用作退火朗之万采样框架中的先验项，从而实现对黑箱功能目标的无梯度优化。我们通过多种应用场景验证了运动学组件拼装的普适性，包括基于用户选择或自动检索部件实例化运动学图、合成具有用户定义功能性的装配体，以及通过图编辑实现关节运动重定向。