Title: Puppeteer: Rig and Animate Your 3D Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Automatic rigging

3D animation

auto-regressive generation
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 \textbf{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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Puppeteer, a comprehensive pipeline that automates both rigging and animation for diverse 3D models. The framework consists of three core stages: (1) an autoregressive transformer for skeleton prediction using a joint-based tokenization and hierarchical ordering strategy; (2) an attention-based architecture for skinning weight estimation enhanced by a novel topology-aware joint attention mechanism; and (3) a differentiable optimization module that aligns mesh motion with reference videos generated from text prompts using off-the-shelf video generation models. To support learning, the authors construct Articulation-XL2.0, an expanded dataset of 59.4k rigged models including 11.4k diverse-pose samples. Puppeteer outperforms existing approaches across multiple benchmarks on both rigging and animation tasks and offers significantly improved generalization and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\nThe proposed pipeline is technically sound, well-motivated, and systematically engineered.\n\nThe use of joint-based tokenization combined with hierarchical and randomized ordering improves the coherence and compactness of skeleton sequences.\n\nThe topology-aware joint attention improves skinning weight accuracy by explicitly modeling joint relationships.\n\nEvaluation is well done: multiple benchmarks, detailed quantitative and qualitative comparisons, and ablations are provided.\n\n**Weaknesses:**\n\nThe animation stage is optimization-based and not learnable, limiting the inference speed.\nThe method relies heavily on the quality of the generated video; if the video is unrealistic or noisy, animation fidelity may degrade.\nThere are a few recent works that seem relevant and may provide useful points of comparison or inspiration:\n[1] Zhang, Hao, et al. \"Magicpose4d: Crafting articulated models with appearance and motion control.\" arXiv preprint arXiv:2405.14017 (2024).\n[2] Zhang, Jia-Peng, et al. \"One Model to Rig Them All: Diverse Skeleton Rigging with UniRig.\" arXiv preprint arXiv:2504.12451 (2025).\n\nQUESTIONS:\nHandling Complex Motions: The method relies on video generation models to provide motion guidance. However, these models may struggle to generate complex or large-scale motions (e.g., dancing). Can the authors comment on how well their method handles such cases, and whether this limits the animation quality?\n\nRobustness and Failure Cases: The animation quality seems to depend on several factors, such as the accuracy of the rigging, the viewpoint of the first rendered frame, and whether important parts (e.g., limbs) are always visible in the video. Are there common failure cases when one of these steps goes wrong? It would be helpful if the authors could show or discuss a few examples where the method fails.\n\nLIMITATIONS:\nThe limitations are adequately acknowledged, with additional discussions included in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Puppeteer, a unified pipeline for automatically rigging and animating 3D models. It first predicts joint-based skeletons using an auto-regressive transformer, then computes skinning weights using a topology-aware attention network. To animate the model, it leverages reference videos generated from text prompts (e.g., using Kling AI), using a differentiable optimization method to align 3D motion (each frame has its own set of the learnable parameters of rotation and translation of the bones) to the video. The authors also expand the Articulation-XL dataset to 59.4k rigged models. Experiments show strong improvements in rigging accuracy, skinning quality, and animation stability compared to state-of-the-art methods.\n\nSTRENGTHS AND WEAKNESSES:\nQuality:\n- The paper presents a strong and well-engineered pipeline that unifies skeleton generation, skinning weight prediction, and animation. Each stage is supported by thoughtful architectural design and ablation studies. \n- The extension of the Articulation-XL dataset to 59.4k models (including 11.4k pose-varied examples) offers a meaningful contribution to 3D articulation research. \n- Quantitative results demonstrate state-of-the-art performance across multiple benchmarks, and inference efficiency is significantly improved over prior methods. \n- However, the animation module's runtime and robustness to imperfect video prompts are not clearly reported, and the absence of quantitative animation metrics (e.g., perceptual motion realism) is a notable gap.\n\nClarity\n- The paper is generally well-written and technically clear. However, several clarity issues hinder full comprehension:\n- The introduction references a \"data scarcity limitation of previous approaches,\" but it's unclear whether MagicArticulate is included in this group -- even though it already introduced the original Articulation-XL dataset.\n- The paper extends Articulation-XL, but the citation to MagicArticulate is missing in the introduction, which could confuse readers not already familiar with the dataset’s history.\n- In Section 3.2, there appears to be a contradiction between L143–153 and L195–199: first arguing that joint-based tokenization is more compact, but then noting that bone-based coordinates improve performance. A footnote or brief clarification could help reconcile this.\n- Since this work is heavily based on MagicArticulate, a summary table comparing the two frameworks would greatly benefit readers, helping them understand what has been kept, changed, or added.\n\nSignificance:\n- The paper addresses a high-impact problem in automating the animation pipeline for 3D content creation -- spanning gaming, simulation, and AR/VR. \n- The integration of animation guided by text-to-video generation makes this work more usable and complete than prior rigging-only methods. \n- The extended dataset and performance improvements across rigging tasks may benefit downstream research in generative 3D modeling, especially for non-human and AI-generated shapes.\n\nOriginality:\n- While the paper introduces new techniques (such as joint-based tokenization with hierarchical ordering and topology-aware joint attention for skinning), its core contributions build heavily on MagicArticulate. The overall structure, training strategy, and model formulation are evolutionary rather than revolutionary. \n- The most original addition is the animation module, which transforms rigged models into animated ones using differentiable optimization with video guidance. This makes the framework end-to-end for the first time, which is a meaningful advancement.\n\nWeaknesses:\n- Missing citation and dataset lineage clarification in the introduction reduces clarity.\n- The methodology section does not reconcile the joint-based vs. bone-based tension clearly.\n- The reliance on off-the-shelf video generation is not analyzed for failure cases.\n- No analysis or footnote distinguishes what part of the pipeline is reused vs. redesigned from MagicArticulate.\n- Evaluation lacks perceptual animation metrics or user studies (Sec 5.4 all about qualitative experiment).\n- No runtime or compute cost is reported for the animation phase.\n\nQUESTIONS:\n1. The authors mention that previous approaches suffered from data scarcity (L45), but MagicArticulate already proposed the original Articulation-XL dataset. Are you referring to MagicArticulate as data-scarce, or only methods before it? \n2. Joint vs. bone inconsistency: In L143–153, you argue for joint-based tokenization due to compactness and lower redundancy. However, in L195–199, you state that bone-based coordinates yield better performance. Can you clarify better?\n3. Comparison to MagicArticulate: The work is heavily based on MagicArticulate (autoregressive skeletons, Articulation-XL, point-cloud conditioning), a side-by-side comparison table or architectural diagram would help clarify what has changed or improved. \n4. Robustness to video quality: Your animation pipeline depends on video prompts generated via text-to-video models. What happens when the generated video contains deformations, or irrelevant motion? Is your optimization robust to such cases?\n5. Animation runtime and scalability: How long does the animation optimization take for typical sequences, and how does it scale with mesh complexity or video length? Could this be practical for real-time or interactive applications?\n\nLIMITATIONS:\nGoing to recap what the authors mentioned in the Appendix: \n- Despite its strong performance, Puppeteer has two main limitations. \n- First, it does not capture very fine-scale deformations, such as flowing hair or fluttering cloth, because no skeletons are generated for these highly deformable parts. \n- Second, the animation stage still relies on per-scene optimization, which limits real-time deployment.\n\nOther limitations can be referred to weaknesses above (if appropriate).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposed a framework for automating the process of the rigging and animation using following key components. \n1. Automating Rigging:\n- A newly curated dataset\n- Novel joint-based skeleton representation\n- Sequence radomization incorporating target-aware positional indicator \n- Autoregressive skeleton generation\n\n2. Skinning Weight Prediction: \n- Multi attention processes to integrate global shape context into refined bones and point features for precise skinning weight prediction. \n\n3. Text-to-video generation guided animation \n- Using text-to-video models as a guidance, the skeleton pose and root movement of the 3D model are generated.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\nThe paper is well-written and the method is clear and easy to follow. Experiments and results are extensive and demonstrate significant improvements over baselines.\n\nWeakness\nLittle explanation about dataset curation and information. There's no detailed explanation about the Articulation-XL2.0 dataset. I want to know precisely which data types were added and how their inclusion specifically impacted the model's performance. Additionally, incorporating \"diverse-pose subset\" makes generalization to novel articulations. But what kind of new poses used is not mentioned or visualized.\n\nQUESTIONS:\n1. Regarding Shape-conditioned auto-regressive generation, where does ground truth T come from? All ground truth data are preprocessed from Articulation-XL2.0?\n\n2. Incorporating \"diverse-pose subset\" makes generalization to novel articulations. But what kind of new poses used is not mentioned or visualized.  Could you provide more explanation about this part?\n\n3. There's no detailed explanation about the Articulation-XL2.0. I want to know precisely which data types were added and how their inclusion specifically impacted the model's performance. \n\n4. The joint-based tokenization discretizes joint coordinates into a grid. I would be curious about whether there's any potential issues about spatial information loss due to this process.\n\n5. Topology-aware Joint Attention use graph distance. How this graph distance is calculated for very complex skeletons? \n\n6. I think video quality from text2video generation model is critical for animation quality.  Can these video models make very complex moves well? Or maybe the video is the weak part for final animation quality? I think this can be.\n\n7. In experiment, the paper said that RigNet struggles with diverse object categories and fails to converge well. Is RigNet design bad? Or just the dataset too hard for RigNet?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a comprehensive framework for automatic rigging and animation of diverse 3D objects. The\ncontributions include an expanded dataset (Articulation-XL2.0), an auto-regressive transformer for skeleton\ngeneration, a novel topology-aware attention-based architecture for skinning weight prediction, and a\ndifferentiable optimization-based method for animation. Extensive experiments demonstrate the effectiveness of\nthe proposed approach, showing consistent improvements in both performance and efficiency over existing\nbaselines.\n\nSTRENGTHS AND WEAKNESSES:\nQuality\nThis work presents a complete pipeline, along with detailed technical descriptions, ablation studies, different\ntesting , and supporting qualitative evidence through figures and videos. However, some of the authors’ claims\nwould benefit from stronger empirical or theoretical support. We refer to the limitations section in particular, where\nfurther clarification or justification would strengthen the overall contribution.\nClarity\nThe paper is well-organized, with clear structure and informative figures. The authors provide a detailed\npresentation of Data expansion methodological components, technical tools, and implementation details.\nSupplementary materials such as images and videos are also provided to support the claims. However, certain\nterms are ambiguously describe，like \"randomized valid poses\" in 3.1 Dataset: Articulation-XL2.0.\nSignificance\nThe introduction of a new/expanded dataset especially the pipeline represents a meaningful contribution to the\ncommunity and is likely to benefit future research in this area.\nOriginality\nThe authors propose a comprehensive framework that addresses both automatic rigging and animation for\ndiverse 3D objects. The contributions include: (1) a more diverse dataset (Articulation-XL2.0); (2) an improved\nauto-regressive transformer for skeleton generation; (3) a novel attention-based architecture for skinning weight\nprediction; and (4) a differentiable optimization-based animation method that requires no neural network\nparameters.\nThe methods for dataset construction and auto-regressive skeleton generation relies heavily on prior techniques.\nThe skeleton generation component appears to be an incremental extension of prior methods, with all three\nvariants being derived from similar approaches based on auto-regressive transformers. The deeper insights\nprimarily emerge through the ablation studies and limited visualizations.\nFor the animation part, the authors introduce a differentiable optimization-based approach that directly optimizes a\nsequence of motion parameters without relying on neural networks. While this idea is promising and shows\nimproved performance over baselines—likely due to the use of joint-specific transformations enabled by the\nlearned skeleton—the paper lacks sufficient discussion of this component. Notably, the approach bears\nconceptual similarity to existing works such as some work in 4DGS fileds, which employs a deformation network\nto predict motion parameters. These related methods all aim to fit deformation/motion parameters, yet the paper\nlacks any meaningful discussion of this shared strategy. In fact, this design choice—particularly the focus on jointlevel\nfitting—may partly explain the method’s limitations in handling fine-scale deformations. Furthermore, in\nSection 4, the authors primarily describe the optimization procedure, but the actual animation process is not\nclearly presented, nor are there references to prior work that would help clarify it.\n\nQUESTIONS:\nFor the animation part, the optimization framework incorporates seven different loss. However, the paper lacks\nfurther discussion, like the necessity of the balance of different loss.\nCould you provide more information or example about the long sequence or the complex motion sequences\nand humanoid morphologies sequences/video. Most examples in the appendix are animal related.\nCould you provide more analysis about why better performance for Spatial order in Articulation-XL2.0.\n(Not Important ) While the limitations regarding fine-scale deformations are acknowledged in the paper, we\nwould appreciate the inclusion of relevant visualizations to better illustrate these shortcomings. Additionally, a\nfair comparison of the optimization cost with baseline methods would strengthen the evaluation and provide a\nclearer understanding of the trade-offs involved.\n\nLIMITATIONS:\nWhile the authors have acknowledged certain limitations, some of the claims—such as the applicability to\ncomplex motion sequences—lack sufficient empirical evidence.\nIn the dataset section (Articulation-XL2.0), some descriptions are vague, such as the term \"randomized valid\nposes\". In Sec.4, the authors primarily describe the optimization procedure, but the actual animation process is\nnot clearly presented, nor are there references to prior work that would help clarify it.\nAlthough the dataset aims to increase diversity by incorporating more animal morphologies, the supplementary\nvideos predominantly showcase animal examples, while humanoid morphologies (video) appear\nunderrepresented. Given the stated goal of generality, we believe both categories are equally important and\nshould be demonstrated.\nThe core methodology appears to be largely composed of existing techniques, and the validation is mostly limited\nto ablation studies and selected qualitative examples. While helpful, the paper would benefit from deeper, more\ninsightful analysis. Some discussions (e.g., about ordering) have also been covered in previous works.\nNotably, the spatial ordering performs well on Articulation-XL2.0, but the underlying reason for this improved\nperformance remains unclear. A deeper exploration into why spatial ordering works better in this context would be\nvaluable.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}