标题： PhysRig：用于真实关节物体建模的可微分物理基础皮肤和绑定框架 显示英文标题

标题： PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling

摘要： 皮肤和骨骼绑定是动画、刚体物体重建、运动转移和4D生成中的基本组成部分。 现有方法主要依赖于线性混合皮肤（LBS），因为其简单性和可微性。 然而，LBS会引入体积损失和不自然的变形等伪影，并且无法模拟像软组织、毛发和柔性附肢（例如，大象的鼻子、耳朵和脂肪组织）这样的弹性材料。 在本工作中，我们提出了PhysRig：一种可微的基于物理的皮肤和骨骼绑定框架，通过将刚性骨架嵌入到体积表示（例如，四面体网格）中，该网格被模拟为由动画骨架驱动的可变形软体结构，从而克服这些限制。 我们的方法利用连续力学，并将物体离散化为嵌入欧拉背景网格中的粒子，以确保对材料属性和骨骼运动的可微性。 此外，我们引入了材料原型，显著减少了学习空间，同时保持了高表达性。 为了评估我们的框架，我们使用来自Objaverse、The Amazing Animals Zoo和MixaMo的网格构建了一个综合的合成数据集，涵盖了多种物体类别和运动模式。 我们的方法始终优于传统的基于LBS的方法，生成更真实和物理上合理的结果。 此外，我们展示了我们框架在姿态迁移任务中的适用性，突显了其在刚体物体建模中的多功能性。 显示英文摘要

摘要： Skinning and rigging are fundamental components in animation, articulated object reconstruction, motion transfer, and 4D generation. Existing approaches predominantly rely on Linear Blend Skinning (LBS), due to its simplicity and differentiability. However, LBS introduces artifacts such as volume loss and unnatural deformations, and it fails to model elastic materials like soft tissues, fur, and flexible appendages (e.g., elephant trunks, ears, and fatty tissues). In this work, we propose PhysRig: a differentiable physics-based skinning and rigging framework that overcomes these limitations by embedding the rigid skeleton into a volumetric representation (e.g., a tetrahedral mesh), which is simulated as a deformable soft-body structure driven by the animated skeleton. Our method leverages continuum mechanics and discretizes the object as particles embedded in an Eulerian background grid to ensure differentiability with respect to both material properties and skeletal motion. Additionally, we introduce material prototypes, significantly reducing the learning space while maintaining high expressiveness. To evaluate our framework, we construct a comprehensive synthetic dataset using meshes from Objaverse, The Amazing Animals Zoo, and MixaMo, covering diverse object categories and motion patterns. Our method consistently outperforms traditional LBS-based approaches, generating more realistic and physically plausible results. Furthermore, we demonstrate the applicability of our framework in the pose transfer task highlighting its versatility for articulated object modeling.