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RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets

Isabella Liu, Zhan Xu, Wang Yifan, Hao Tan, Zexiang Xu, Xiaolong Wang, Hao Su, Zifan Shi

arXiv:2502.09615v2cs.CV

TL;DR

Automatic rigging must handle diverse object topologies without the category-specific templates used by many existing methods. RigAnything addresses this with a template-free autoregressive transformer that generates skeletons and skinning weights, achieving state-of-the-art performance across diverse assets while improving efficiency. Its main scope boundary is limited control over rig detail and occasional ambiguity from geometry-only inference.

  • Problem

    Existing auto-rigging methods rely heavily on predefined templates, while diverse shapes and configurations require template-free skeleton and skinning prediction.

  • Method

    RigAnything serializes tree-structured skeletons in BFS order and autoregressively predicts joints, connections, and skinning weights from shape geometry.

  • Results

    RigAnything achieves state-of-the-art auto-rigging performance, surpassing prior methods in quality, robustness, generalizability, and efficiency across diverse object types.

  • Takeaways & Limitations

    The method supports end-to-end template-free rigging across varied object categories and configurations.

  • Takeaways & Limitations

    RigAnything does not control rig detail levels and can face ambiguities because it relies solely on geometry information.

Abstract

from arXiv · show

We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints and skeleton topologies and assigning skinning weights in a template-free manner. Unlike most existing auto-rigging methods, which rely on predefined skeleton templates and are limited to specific categories like humanoid, RigAnything approaches the rigging problem in an autoregressive manner, iteratively predicting the next joint based on the global input shape and the previous prediction. While autoregressive models are typically used to generate sequential data, RigAnything extends its application to effectively learn and represent skeletons, which are inherently tree structures. To achieve this, we organize the joints in a breadth-first search (BFS) order, enabling the skeleton to be defined as a sequence of 3D locations and the parent index. Furthermore, our model improves the accuracy of position prediction by leveraging diffusion modeling, ensuring precise and consistent placement of joints within the hierarchy. This formulation allows the autoregressive model to efficiently capture both spatial and hierarchical relationships within the skeleton. Trained end-to-end on both RigNet and Objaverse datasets, RigAnything demonstrates state-of-the-art performance across diverse object types, including humanoids, quadrupeds, marine creatures, insects, and many more, surpassing prior methods in quality, robustness, generalizability, and efficiency. It achieves significantly faster performance than existing auto-rigging methods, completing rigging in under a few seconds per shape. Please check our website for more details: https://www.liuisabella.com/RigAnything

1 Introduction

RigAnything targets template-free automatic rigging for diverse 3D assets by autoregressively generating skeletons and skinning weights. Trained on broad data, it reports state-of-the-art quality, robustness, generalizability, and efficiency.

  • Rigging provides fine-grained control over degrees of freedom and range of motion, addressing limitations of text-based and video-guided animation control.
  • Existing auto-rigging methods often depend on predefined skeleton templates, limiting applicability to specific categories such as humanoids.
  • RigAnything probabilistically grows a skeleton from the root joint and infers skinning weights by holistically considering all joints.
  • BFS ordering represents each tree-structured skeleton as a sequence of 3D joint positions and parent indices, supporting arbitrary structures and joint counts without templates.
  • Diffusion sampling predicts continuously valued joint positions, while transformer blocks capture global shape and hierarchical relationships.
  • Training combines RigNet with 9,686 curated Objaverse shapes spanning varied object types and poses, supporting broad generalizability.
  • Extensive experiments report state-of-the-art auto-rigging performance, surpassing prior methods in quality, robustness, generalizability, and efficiency.

2 Related Work

Prior automatic rigging methods commonly use templates or costly non-differentiable procedures, while RigAnything uses autoregressive modeling to support diverse skeleton structures and object categories.

  • Traditional rigging methods optimize predefined skeleton variations, which is computationally expensive and reduces generalizability.
  • Template-based methods are largely confined to humanoid characters in standard poses, limiting robustness across objects, poses, and skeleton topologies.
  • RigNet supports varying joint counts and topologies without templates but uses clustering and Minimum Spanning Tree operations, preventing end-to-end training and limiting efficiency.
  • Autoregressive models have been applied broadly in language, vision, and 3D shape generation, including hierarchical and semantically aligned shape representations.

3 Method

RigAnything converts tree-structured skeletons into BFS-ordered sequences and autoregressively predicts joints, parent connections, and skinning weights without predefined templates. Diffusion modeling handles continuous joint positions while transformer features capture shape context and evolving skeleton structure.

  • The method targets animatable assets by generating plausible skeletons and corresponding skinning weights for articulation with Linear Blend Skinning.
  • Autoregressive Skeleton Prediction: BFS serialization represents each joint with a 3D position and parent index, with the root first and earlier parent indices enforcing hierarchy.
  • Skeleton Ambiguity: Randomized same-depth ordering and generative modeling address sibling ambiguity, allowing multiple valid skeleton sequences for the same shape.
  • Autoregressive Skeleton Prediction: The autoregressive model predicts each next joint and parent conditionally on prior skeleton elements and global shape tokens, while transformer blocks encode their evolving structural context.
  • Joint Prediction with Diffusion Model: A diffusion sampling process predicts continuously valued joint positions by learning to denoise perturbed joints conditioned on the current skeleton state and input shape.
  • Skinning Weight Prediction: Skinning weights are inferred from shape and skeleton tokens through per-point, pair-wise joint influence prediction trained with weighted cross-entropy.

4.1 Implementation Details

RigAnything uses a transformer with hybrid attention and diffusion-based joint prediction to autoregressively generate skeletons, then updates context for connectivity and skinning. Inference starts from a learned token and terminates when the predicted parent indicates sequence completion.

  • Architecture: The model uses 12 transformer layers with hidden dimension 1024, 16 attention heads, and 4096-dimensional MLPs.The implementation also uses Pre-Layer Normalization, Layer Normalization, residual connections, and GeLU activation.
  • Joint diffusion: Joint diffusion uses a cosine noise scheduler with 1000 training steps and 50 inference resampling steps.The denoising MLP is conditioned on transformer-produced joint tokens through AdaLN.
  • Inference pipeline: During inference, diffusion-sampled joint positions are fused with shape and context tokens to update connectivity and skinning predictions.The fusing module has input size 3072 and hidden dimensions 2048 and 1024.
  • Autoregressive generation: A learnable BOS token starts each BFS-ordered skeleton sequence, while a self-parent prediction signals that generation should stop.The joint sampled from the BOS token is treated as the root joint.

4.2 Dataset

The dataset combines RigNet with a rigorously filtered Objaverse collection spanning varied articulated and rigid object categories. Online pose augmentation and validity filtering are used to improve robustness and ensure reliable skeleton annotations.

  • Dataset composition: RigAnything uses RigNet and a filtered Objaverse collection covering bipedal, quadrupedal, avian, marine, insectoid, and manipulable rigid objects.The filtered data is organized into six categories, including humanoid/bipedal, quadruped, insectoid, avian, marine, and other.
  • Pose augmentation: Online pose augmentation randomly perturbs joints by up to 45 degrees and deforms the input point cloud using the perturbed skeleton and ground-truth skinning.The augmentation improves generalization to objects in different poses.
  • Data filtering: Objaverse filtering removes shapes with more than 64 joints, invalid hierarchies, poor geometry alignment, or overly simplified geometry.These rules target skeleton validity, geometry correspondence, and usable shape complexity.
  • Data filtering: The original Objaverse rigging subset contains 21,622 shapes, and filtering yields 12,040 shapes with reliable rigging information.The process removes 811 overly complex skeletons and 10,471 shapes with low-quality annotations.
  • Dataset statistics: The dataset includes varied joint-count distributions, with the category statistics summarized in Table 2 and joint-count distribution visualized in Fig. 9.The supplied passages identify the statistics and distribution analyses but do not provide their complete numerical breakdown.

4.3 Evaluation and Baseline Comparisons

RigAnything is evaluated against RigNet and humanoid-specific baselines for skeleton, connectivity, and skinning prediction. Across these evaluations, it produces more accurate skeletons, improves connectivity metrics, and yields more consistent skinning weights.

  • Qualitative comparison: Qualitative comparisons show reasonable joint counts and skeletons aligned with underlying shapes, whereas RigNet struggles with uncommon shapes and often generates excessive joints.The examples include characters with tails or wings.
  • Skeleton prediction: RigAnything produces skeletons that align more closely with ground truth than the baselines across the reported bone-matching and Chamfer-distance metrics.Evaluation uses IoU, precision, recall, CD-J2J, CD-B2B, and CD-J2B.
  • Humanoid comparison: RigAnything significantly outperforms TARig and NBS on skeleton prediction for the humanoid subset.The comparison uses the original evaluation sets, and neither baseline can be fine-tuned because required training or processing scripts are unavailable.
  • Connectivity prediction: RigAnything significantly outperforms RigNet across connectivity classification accuracy, bone Chamfer distance, and edit distance.Connectivity is evaluated with ground-truth joints rather than predicted joints.
  • Skinning prediction: RigAnything achieves higher precision and lower average L1 error for skinning-weight prediction than the compared baselines.Qualitatively, it better distinguishes regions that are close in Euclidean space but far apart geodesically.

4.4 Ablation Study

Ablations show that joint diffusion, point normals, and pose augmentation each contribute to skeleton or skinning performance. Joint diffusion avoids collapsed joint positions, while pose augmentation improves alignment on unseen poses.

  • Joint diffusion: Replacing joint diffusion with deterministic L2 loss causes predicted joints to collapse toward mean positions.The full probabilistic diffusion model accounts for structural ambiguities such as equivalent sibling orderings.
  • Additional results: Additional qualitative results are shown for shapes reconstructed from casual images and for the RigNet dataset.The casual-image examples first use an off-the-shelf image-to-3D pipeline to generate shapes.
  • Joint diffusion: Joint diffusion improves skeleton IoU by almost two times in the ablation study.The reported comparison concerns the skeleton IoU metric.
  • Normal injection: Removing point normals causes a significant decline in skeleton performance, showing that normals provide useful geometric information.Normal injection also improves geodesic inference and skinning prediction.
  • Pose augmentation: Pose augmentation improves skeleton prediction and produces better-aligned structures on character poses not present in the dataset.The augmentation randomly deforms input point clouds using ground-truth skeletons and skinning.

5 Limitation and Future Work

RigAnything can rig diverse objects, but it does not yet provide artist control over rig detail and faces ambiguities when geometry lacks sufficient structural cues. Future work targets controllable detail, additional texture cues, and motion-style information.

  • 5 Limitation and Future Work: RigAnything produces more accurate and consistent skinning weights in challenging cases with large geodesic distances.The comparison is reported in Fig. 12.
  • 5 Limitation and Future Work: RigAnything does not allow artists to control the level of detail in generated rigs.More detailed rigging data and a network condition could support finer rigging in areas such as heads and hands.
  • 5 Limitation and Future Work: Geometry-only inference can lack sufficient cues for rig structure, creating ambiguities.The authors propose incorporating texture information as an additional cue.
  • 5 Limitation and Future Work: Skinning weight prediction does not account for motion styles influenced by materials.The authors suggest dynamic training data, although high-quality dynamic data are scarce.

6 Conclusion

RigAnything predicts rigs for 3D assets with an autoregressive transformer-based method that probabilistically predicts skeletons and assigns skinning weights without templates. Trained end-to-end on RigNet and Objaverse, it performs effectively and generalizes across diverse object categories.

  • 6 Conclusion: RigAnything automatically predicts rigs for 3D assets using an autoregressive transformer-based method.The method addresses diverse topologies and rigging ambiguities through probabilistic prediction.
  • 6 Conclusion: The method predicts skeletons and assigns skinning weights without relying on templates.This enables end-to-end training on RigNet and the diverse Objaverse dataset.
  • 6 Conclusion: RigAnything shows effectiveness and generalizability across a wide range of object categories.The conclusion describes this as superiority across diverse object types.
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