Make-It-Animatable：一种用于创建动画就绪3D角色的高效框架

Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters

November 27, 2024

作者: Zhiyang Guo, Jinxu Xiang, Kai Ma, Wengang Zhou, Houqiang Li, Ran Zhang

cs.AI

摘要

现代创意产业中，3D角色至关重要，但使它们具有动画性往往需要大量手动工作，如绑定和蒙皮。现有的自动绑定工具存在一些限制，包括需要手动注释、刚性骨架拓扑结构以及在不同形状和姿势之间的有限泛化能力。另一种方法是生成可动画化的化身，预先绑定到一个带有骨骼模板的网格上。然而，这种方法通常缺乏灵活性，通常仅限于逼真的人体形状。为了解决这些问题，我们提出了Make-It-Animatable，这是一种新颖的数据驱动方法，可以使任何3D人形模型在不到一秒的时间内准备好进行角色动画，无论其形状和姿势如何。我们的统一框架生成高质量的混合权重、骨骼和姿势变换。通过结合基于粒子的形状自动编码器，我们的方法支持各种3D表示，包括网格和3D高斯斑点。此外，我们采用粗到细的表示和结构感知建模策略，以确保对具有非标准骨架结构的角色的准确性和鲁棒性。我们进行了大量实验证明我们框架的有效性。与现有方法相比，我们的方法在质量和速度上都取得了显著改进。

English

3D characters are essential to modern creative industries, but making them animatable often demands extensive manual work in tasks like rigging and skinning. Existing automatic rigging tools face several limitations, including the necessity for manual annotations, rigid skeleton topologies, and limited generalization across diverse shapes and poses. An alternative approach is to generate animatable avatars pre-bound to a rigged template mesh. However, this method often lacks flexibility and is typically limited to realistic human shapes. To address these issues, we present Make-It-Animatable, a novel data-driven method to make any 3D humanoid model ready for character animation in less than one second, regardless of its shapes and poses. Our unified framework generates high-quality blend weights, bones, and pose transformations. By incorporating a particle-based shape autoencoder, our approach supports various 3D representations, including meshes and 3D Gaussian splats. Additionally, we employ a coarse-to-fine representation and a structure-aware modeling strategy to ensure both accuracy and robustness, even for characters with non-standard skeleton structures. We conducted extensive experiments to validate our framework's effectiveness. Compared to existing methods, our approach demonstrates significant improvements in both quality and speed.