用物理模拟替代传统皮肤绑定,让动画更真实自然。
PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling
- 将骨架嵌入体积网格,通过连续介质力学模拟软体变形。
- 在多个数据集上优于传统LBS方法,生成更真实的形变效果。
- 适合需要高保真动画的影视建模与角色驱动任务。
皮肤绑定与骨架设置是动画、可动物体重建、动作迁移和4D生成的核心技术。现有方法主要依赖线性混合皮肤(LBS),因其简单且可微分。但LBS会导致体积丢失和不自然形变,无法模拟软组织、毛发或柔性附肢(如象鼻、耳朵和脂肪组织)。本文提出PhysRig:一种可微分的物理驱动皮肤绑定框架,将刚性骨架嵌入四面体网格等体积表示中,作为受骨架驱动的可变形软体结构进行模拟。方法基于连续介质力学,将物体离散为嵌入欧拉背景网格的粒子,确保对材料属性和骨骼运动的可微性。同时引入材料原型,显著降低学习空间而保持高表达能力。我们构建了综合合成数据集,涵盖Objaverse、The Amazing Animals Zoo和MixaMo中的多种物体类别与运动模式。实验表明,该方法在多组对比中持续优于传统LBS,生成更真实且物理合理的形变结果,并在姿态迁移任务中验证了其在可动物体建模中的广泛应用潜力。
原文摘要 · Abstract (English)
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.
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