Puppeteer: Rig and Animate Your 3D Models

• URL: http://arxiv.org/abs/2508.10898v1
• Date: Thu, 14 Aug 2025 17:59:31 GMT
• Title: Puppeteer: Rig and Animate Your 3D Models
• Abstract summary: Puppeteer is a comprehensive framework that addresses both automatic rigging and animation for diverse 3D objects.<n>Our system first predicts plausible skeletal structures via an auto-regressive transformer.<n>It then infers skinning weights via an attention-based architecture.
• Score: 105.11046762553121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• AnimaSpark: A Feed-Forward Method for Animating Arbitrary 3D Objects [9.201202105097991]
  Current methods for category-agnostic animation generation exhibit critical limitations in inference speed, motion quality, and adherence to textual prompts.<n>This paper introduces AnimaSpark, a novel pipeline for category-agnostic 3D animation generation.<n>Our method achieves superior performance over existing state-of-the-art techniques across key metrics, including text-motion alignment, quality of motion, and computational efficiency.
  arXiv  Detail & Related papers  (2026-06-09T15:25:39Z)
• MotionDreamer: Universal Skeletal Motion Generation for 3D Rigged Shapes [52.45449797329548]
  MotionDreamer is a diffusion-based framework for category-agnostic skeletal animation generation from 2D video guidance.<n>We propose a structural-semantic injection mechanism to bridge the gap between 2D visual motion cues and heterogeneous 3D skeletal structures.<n>Our approach significantly outperforms existing methods, setting a new state-of-the-art benchmark for robust and efficient 4D asset generation.
  arXiv  Detail & Related papers  (2026-06-01T00:42:31Z)
• Rigel3D: Rig-aware Latents for Animation-Ready 3D Asset Generation [6.821657791615377]
  Rigel3D is a generative method for animation-ready 3D assets represented as rigged meshes.<n>A rig-aware autoencoder decodes these representations into mesh geometry, skeleton topology, joint coordinates, and skinning weights.<n>A two-stage latent generative model synthesizes both surface and skeleton representations for image-conditioned generation.
  arXiv  Detail & Related papers  (2026-05-13T07:55:29Z)
• AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation [64.1405325883561]
  We present AniGen, a unified framework that directly generates animate-ready 3D assets conditioned on a single image.<n>Our key insight is to represent shape, skeleton, and skinning as mutually consistent $S3$ Fields (Shape, Skeleton, Skin) defined over a shared spatial domain.<n>AniGen substantially outperforms state-of-the-art sequential baselines in rig validity and animation quality.
  arXiv  Detail & Related papers  (2026-04-09T20:22:06Z)
• 3DProxyImg: Controllable 3D-Aware Animation Synthesis from Single Image via 2D-3D Aligned Proxy Embedding [46.75707405618843]
  We propose a lightweight 3D animation framework that decouples geometric control from appearance synthesis.<n>Our method achieves efficient animation generation on low-power platforms.
  arXiv  Detail & Related papers  (2025-12-17T06:38:07Z)
• Particulate: Feed-Forward 3D Object Articulation [89.78788418174946]
  Particulate is a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure.<n>We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets.<n>During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds.
  arXiv  Detail & Related papers  (2025-12-12T18:59:51Z)
• UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework [54.337290937468175]
  We propose UniMo, an autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework.<n>We show that our method simultaneously generates corresponding videos and motions while performing accurate motion capture.
  arXiv  Detail & Related papers  (2025-12-03T16:03:18Z)
• Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding [54.859943475818234]
  We present Motion4D, a novel framework that integrates 2D priors from foundation models into a unified 4D Gaussian Splatting representation.<n>Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence.<n>Our method significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis.
  arXiv  Detail & Related papers  (2025-12-03T09:32:56Z)
• MagicArticulate: Make Your 3D Models Articulation-Ready [109.35703811628045]
  We present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets.<n>Our key contributions are threefold. First, we introduce Articulation-averse benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from XL-XL.<n>Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories.
  arXiv  Detail & Related papers  (2025-02-17T18:53:27Z)
• Make-It-Animatable: An Efficient Framework for Authoring Animation-Ready 3D Characters [86.13319549186959]
  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.
  arXiv  Detail & Related papers  (2024-11-27T10:18:06Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention [9.52027244702166]
  Spice-E is a neural network that adds structural guidance to 3D diffusion models. We show that our approach supports a variety of applications, including 3D stylization, semantic shape editing and text-conditional abstraction-to-3D.
  arXiv  Detail & Related papers  (2023-11-29T17:36:49Z)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.