Puppeteer: Rig and Animate Your 3D Models
Authors: name: Chaoyue Song, name: Xiu Li, name: Fan Yang, name: Zhongcong Xu, name: Jiacheng Wei, name: Fayao Liu, name: Jiashi Feng, name: Guosheng Lin, name: Jianfeng Zhang

Modern interactive applications increasingly demand dynamic 3D content, yet
the transformation of static 3D models into animated assets constitutes a
significant bottleneck in content creation pipelines. While recent advances in
generative AI have revolutionized static 3D model creation, rigging and
animation continue to depend heavily on expert intervention. We present
Puppeteer, a comprehensive framework that addresses both automatic rigging and
animation for diverse 3D objects. Our system first predicts plausible skeletal
structures via an auto-regressive transformer that introduces a joint-based
tokenization strategy for compact representation and a hierarchical ordering
methodology with stochastic perturbation that enhances bidirectional learning
capabilities. It then infers skinning weights via an attention-based
architecture incorporating topology-aware joint attention that explicitly
encodes inter-joint relationships based on skeletal graph distances. Finally,
we complement these rigging advances with a differentiable optimization-based
animation pipeline that generates stable, high-fidelity animations while being
computationally more efficient than existing approaches. Extensive evaluations
across multiple benchmarks demonstrate that our method significantly
outperforms state-of-the-art techniques in both skeletal prediction accuracy
and skinning quality. The system robustly processes diverse 3D content, ranging
from professionally designed game assets to AI-generated shapes, producing
temporally coherent animations that eliminate the jittering issues common in
existing methods.