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.
3D 角色是现代创意产业的重要组成部分，但使其具有可动画性通常需要大量的手动工作，例如绑定骨架（rigging）和蒙皮（skinning）。现有的自动绑定工具存在多种局限性，包括需要手动标注、骨架拓扑结构固定，以及在多样化形状和姿势上的泛化能力有限。另一种替代方法是生成预绑定到骨架模板网格的可动画化身，但这种方法通常缺乏灵活性，并且通常仅限于逼真的人类形状。 为了解决这些问题，我们提出了 Make-It-Animatable，一种新颖的数据驱动方法，能够在不到一秒的时间内使任何 3D 人形模型准备好用于角色动画，而不受其形状和姿势的限制。我们的统一框架能够生成高质量的混合权重（blend weights）、骨骼以及姿势变换。通过结合基于粒子的形状自动编码器（particle-based shape autoencoder），该方法支持多种 3D 表示形式，包括网格和 3D 高斯投影（Gaussian splats）。 此外，我们采用了粗到细的表示方法和结构感知建模策略，确保即使对于具有非标准骨架结构的角色，也能实现准确性和鲁棒性。我们进行了广泛的实验验证了该框架的有效性。与现有方法相比，我们的方法在质量和速度上均表现出显著提升，为多样化的 3D 角色动画制作提供了高效的解决方案。