基于伪影驱动的运动学先验细化的蒙皮运动重定向
Skinned Motion Retargeting via Artifact-driven Kinematic Prior Refinement
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中文总结 AI 辅助
提出一种几何感知的运动重定向框架，通过伪影驱动的运动学先验细化显式解决目标侧自穿透伪影，提升跨骨架重定向精度与泛化能力。
中文摘要 AI 辅助
运动重定向旨在将源运动迁移到具有不同骨骼结构、比例和身体形状的目标角色上。尽管最近的神经重定向方法提高了跨不同骨架的灵活性，但目标侧的几何伪影（如自穿透）仍然难以解决。具体而言，现有的几何感知方法通常依赖固定的骨架模板或隐式几何条件预测，要求单个网络同时考虑目标几何变形、检测目标侧伪影，并仅从目标几何预测相应的修正，这限制了它们在多样化骨架结构和身体形状上的泛化能力。在本文中，我们提出了一种几何感知的运动重定向框架，该框架显式地将姿态角色几何中观察到的伪影与运动细化联系起来，同时保持骨架无关的神经重定向的灵活性。我们的方法首先使用基于Transformer的重定向自编码器学习跨不同骨架共享的运动嵌入，该自编码器可在任意源-目标骨架对之间迁移运动。基于这一运动学运动先验，我们引入了一个伪影驱动的细化模块，该模块观察姿态目标网格上的自穿透，并通过运动到顶点的雅可比矩阵将其转换为修正线索。我们进一步利用基于蒙皮权重的关节对齐几何特征（源自静止姿态网格）对运动解码进行目标几何条件化。该设计在统一框架中结合了显式的目标侧伪影推理和灵活的几何感知解码。在固定和任意骨架结构设置上的实验表明，我们的方法提高了运动学重定向精度并减少了几何伪影，在已见和未见的目标角色上均产生了合理的运动。
英文摘要
Motion retargeting aims to transfer a source motion to target characters with different skeletal structures, proportions, and body shapes. Although recent neural retargeting methods have improved flexibility across diverse skeletons, target-side geometric artifacts such as self-penetration remain difficult to resolve. Specifically, existing geometry-aware approaches often rely on fixed skeleton templates or implicit geometry-conditioned prediction, requiring a single network to account for target geometry deformation, detect target-side artifacts, and predict the corresponding correction from target geometry alone, which limits their ability to generalize across diverse skeleton structures and body shapes. In this paper, we present a geometry-aware motion retargeting framework that explicitly connects artifacts observed in the posed character geometry to motion refinement while preserving the flexibility of skeleton-agnostic neural retargeting. Our method first learns a motion embedding shared across different skeletons using a transformer-based retargeting autoencoder that transfers motion across arbitrary source--target skeleton pairs. Building on this kinematic motion prior, we introduce an artifact-driven refinement module that observes self-penetration on the posed target mesh and converts it into a corrective cue through a motion-to-vertex Jacobian. We further condition motion decoding on target geometry using skinning weight-based joint-aligned geometry features derived from the rest pose mesh. This design combines explicit target-side artifact reasoning with flexible geometry-aware decoding in a unified framework. Experiments on both fixed and arbitrary skeleton structure settings show that our method improves kinematic retargeting accuracy and reduces geometric artifacts, producing plausible motions across seen and unseen target characters.
发表机构
- KAIST（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
