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

• URL: http://arxiv.org/abs/2411.18197v3
• Date: Tue, 11 Mar 2025 07:08:42 GMT
• Title: Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters
• Authors: Zhiyang Guo, Jinxu Xiang, Kai Ma, Wengang Zhou, Houqiang Li, Ran Zhang,
• Abstract summary: 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.<n>Our framework generates high-quality blend weights, bones, and pose transformations.<n>Compared to existing methods, our approach demonstrates significant improvements in both quality and speed.
• Score: 86.13319549186959
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. More demos and code are available at https://jasongzy.github.io/Make-It-Animatable/.

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