Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters
Zhiyang Guo, Jinxu Xiang, Kai Ma, Wengang Zhou, Houqiang Li, Ran Zhang
2024-11-28
Summary
This paper introduces Make-It-Animatable, a new method that makes it quick and easy to prepare 3D characters for animation without needing a lot of manual work.
What's the problem?
Creating 3D characters that can be animated usually requires a lot of time and effort, especially in tasks like rigging (setting up the skeleton) and skinning (making the character's surface move naturally). Existing automatic tools often need manual input, are limited to certain shapes, and can be inflexible, making it hard to animate diverse character designs.
What's the solution?
The authors developed Make-It-Animatable, which uses a data-driven approach to prepare any 3D humanoid model for animation in under one second. Their method generates high-quality components like blend weights and bones automatically. They also use advanced techniques to ensure that the characters look good and move correctly, even if they have unusual shapes or structures. This system can work with various types of 3D representations, making it versatile and efficient.
Why it matters?
This research is significant because it simplifies the animation process for creators in the gaming, film, and animation industries. By reducing the time and effort needed to prepare characters for animation, Make-It-Animatable allows artists to focus more on creativity and storytelling rather than technical details.
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.