Previous motion generation methods are limited to the pre-rigged 3D human
model, hindering their applications in the animation of various non-rigged
characters. In this work, we present TapMo, a Text-driven Animation Pipeline
for synthesizing Motion in a broad spectrum of skeleton-free 3D characters. The
pivotal innovation in TapMo is its use of shape deformation-aware features as a
condition to guide the diffusion model, thereby enabling the generation of
mesh-specific motions for various characters. Specifically, TapMo comprises two
main components - Mesh Handle Predictor and Shape-aware Diffusion Module. Mesh
Handle Predictor predicts the skinning weights and clusters mesh vertices into
adaptive handles for deformation control, which eliminates the need for
traditional skeletal rigging. Shape-aware Motion Diffusion synthesizes motion
with mesh-specific adaptations. This module employs text-guided motions and
mesh features extracted during the first stage, preserving the geometric
integrity of the animations by accounting for the character's shape and
deformation. Trained in a weakly-supervised manner, TapMo can accommodate a
multitude of non-human meshes, both with and without associated text motions.
We demonstrate the effectiveness and generalizability of TapMo through rigorous
qualitative and quantitative experiments. Our results reveal that TapMo
consistently outperforms existing auto-animation methods, delivering
superior-quality animations for both seen or unseen heterogeneous 3D
characters.

通过使用形状变形感知特性作为条件来指导扩散模型，并生成适应不同角色的网格特异运动，从而使 TapMo 成为广泛范围骨骼自由 3D 角色动画合成的一种文本驱动动画流水线。