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.

翻译：3D角色对于现代创意产业至关重要，但使其具备动画能力通常需要在绑定和蒙皮等任务上进行大量手动工作。现有的自动绑定工具面临若干局限，包括需要手动标注、僵化的骨骼拓扑结构，以及对不同形状和姿态的泛化能力有限。另一种方法是生成预先绑定到带骨架模板网格的可动画化身。然而，该方法通常缺乏灵活性，且通常仅限于写实的人体形状。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，无论形状和姿态如何，都能在一秒内使任何3D人形模型准备好进行角色动画。我们的统一框架生成高质量的混合权重、骨骼和姿态变换。通过引入基于粒子的形状自编码器，我们的方法支持包括网格和3D高斯泼溅在内的多种3D表示。此外，我们采用从粗到细的表示和结构感知建模策略，以确保准确性和鲁棒性，即使对于具有非标准骨骼结构的角色也是如此。我们进行了大量实验以验证我们框架的有效性。与现有方法相比，我们的方法在质量和速度上都显示出显著提升。