Puppeteer: Rig and Animate Your 3D Models
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.
689f414aa4caabb4320e5efc
Chaoyue Song
claimed_verified
2025-08-18T06:56:33.666Z
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/avatars/6c8e4a66e1b8b3c786a4000210089392.svg
Chaoyue Song
user
chaoyue7
689f414aa4caabb4320e5efd
Xiu Li
689f414aa4caabb4320e5efe
Fan Yang
689f414aa4caabb4320e5eff
Zhongcong Xu
689f414aa4caabb4320e5f00
Jiacheng Wei
689f414aa4caabb4320e5f01
Fayao Liu
689f414aa4caabb4320e5f02
Jiashi Feng
689f414aa4caabb4320e5f03
Guosheng Lin
689f414aa4caabb4320e5f04
Jianfeng Zhang
Puppeteer: Rig and Animate Your 3D Models
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.
Puppeteer is a framework that automates rigging and animation of 3D models using an auto-regressive transformer, attention-based architecture, and differentiable optimization, outperforming existing methods in accuracy and efficiency.
auto-regressive transformer joint-based tokenization hierarchical ordering stochastic perturbation bidirectional learning attention-based architecture topology-aware joint attention skeletal graph distances differentiable optimization skeletal prediction accuracy skinning quality temporally coherent animations