# MagicArticulate: Make Your 3D Models Articulation-Ready

arXiv: https://arxiv.org/abs/2502.12135 · HTML: https://mlsanity.com/paper/2502.12135/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 52 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets](https://mlsanity.com/paper/2406.13897/) (2024)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://mlsanity.com/paper/2311.15475/) (2023)
- [Michelangelo: Conditional 3D Shape Generation based on Shape-Image-Text Aligned Latent Representation](https://mlsanity.com/paper/2306.17115/) (2023)
- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers](https://mlsanity.com/paper/2406.10163/) (2024)
- [MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenization](https://mlsanity.com/paper/2408.02555/) (2024)
- [MeshXL: Neural Coordinate Field for Generative 3D Foundation Models](https://mlsanity.com/paper/2405.20853/) (2024)
- [EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation](https://mlsanity.com/paper/2409.18114/) (2024)
- [Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations](https://mlsanity.com/paper/2205.02825/) (2022)
- [Learning Skeletal Articulations with Neural Blend Shapes](https://mlsanity.com/paper/2105.02451/) (2021)
- [PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance](https://mlsanity.com/paper/2405.16890/) (2024)
- [REACTO: Reconstructing Articulated Objects from a Single Video](https://mlsanity.com/paper/2404.11151/) (2024)
- [Predicting Animation Skeletons for 3D Articulated Models via Volumetric Nets](https://mlsanity.com/paper/1908.08506/) (2019)
- [Point2Skeleton: Learning Skeletal Representations from Point Clouds](https://mlsanity.com/paper/2012.00230/) (2020)
- [3D Pose Transfer with Correspondence Learning and Mesh Refinement](https://mlsanity.com/paper/2109.15025/) (2021)
- [Skeleton-free Pose Transfer for Stylized 3D Characters](https://mlsanity.com/paper/2208.00790/) (2022)
- [Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction](https://mlsanity.com/paper/2211.10278/) (2022)
- [Morig: Motion-aware rigging of character meshes from point clouds](https://mlsanity.com/paper/2210.09463/) (2022)
- [Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis](https://mlsanity.com/paper/2304.12317/) (2023)
- [MagicPose4D: Crafting Articulated Models with Appearance and Motion Control](https://mlsanity.com/paper/2405.14017/) (2024)
- [SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters](https://mlsanity.com/paper/2203.04746/) (2022)
- [MoDA: Modeling Deformable 3D Objects from Casual Videos](https://mlsanity.com/paper/2304.08279/) (2023)
- [Functional Diffusion](https://mlsanity.com/paper/2311.15435/) (2023)
- [S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video](https://mlsanity.com/paper/2405.12607/) (2024)

## Built on by (most cited first, 30 of 43)

- [Puppeteer: Rig and Animate Your 3D Models](https://mlsanity.com/paper/2508.10898/) (2025)
- [Anymate: A Dataset and Baselines for Learning 3D Object Rigging](https://mlsanity.com/paper/2505.06227/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos](https://mlsanity.com/paper/2506.08334/) (2025)
- [ActionMesh: Animated 3D Mesh Generation with Temporal 3D Diffusion](https://mlsanity.com/paper/2601.16148/) (2026)
- [AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models](https://mlsanity.com/paper/2506.19851/) (2025)
- [Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis](https://mlsanity.com/paper/2601.14253/) (2026)
- [Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation](https://mlsanity.com/paper/2509.12815/) (2025)
- [PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects](https://mlsanity.com/paper/2605.21572/) (2026)
- [PhysRig: Differentiable Physics-Based Skinning and Rigging Framework for Realistic Articulated Object Modeling](https://mlsanity.com/paper/2506.20936/) (2025)
- [Auto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization](https://mlsanity.com/paper/2506.11430/) (2025)
- [HVG-3D: Bridging Real and Simulation Domains for 3D-Conditional Hand-Object Interaction Video Synthesis](https://mlsanity.com/paper/2604.03305/) (2026)
- [One Video, One World: Turning Monocular Video into Physical 4D Scenes](https://mlsanity.com/paper/2606.31388/) (2026)
- [Engine-Native Editable 3D World Reconstruction with Objects and Lighting](https://mlsanity.com/paper/2607.20889/) (2026)
- [Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video Generation](https://mlsanity.com/paper/2509.10687/) (2025)
- [Skin Tokens: A Learned Compact Representation for Unified Autoregressive Rigging](https://mlsanity.com/paper/2602.04805/) (2026)
- [LottieGPT: Tokenizing Vector Animation for Autoregressive Generation](https://mlsanity.com/paper/2604.11792/) (2026)
- [ARMO: Autoregressive Rigging for Multi-Category Objects](https://mlsanity.com/paper/2503.20663/) (2025)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control](https://mlsanity.com/paper/2606.14699/) (2026)
- [RigAnyFace: Scaling Neural Facial Mesh Auto-Rigging with Unlabeled Data](https://mlsanity.com/paper/2511.18601/) (2025)
- [Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models](https://mlsanity.com/paper/2602.09713/) (2026)
- [MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer](https://mlsanity.com/paper/2511.18370/) (2025)
- [MorphGS: Morphology-Adaptive Articulated 3D Motion Transfer from Videos](https://mlsanity.com/paper/2601.02716/) (2026)
- [SPRig: Self-Supervised Pose-Invariant Rigging from Mesh Sequences](https://mlsanity.com/paper/2602.12740/) (2026)
- [SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation](https://mlsanity.com/paper/2603.14152/) (2026)
- [SMAL-pets: SMAL Based Avatars of Pets from Single Image](https://mlsanity.com/paper/2603.17131/) (2026)
- [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026)
- [Fast 4D Mesh Generation by Spatio-Temporal Attention Chains](https://mlsanity.com/paper/2605.19786/) (2026)
- [Adaptive Volumetric Mechanical Property Fields Invariant to Resolution](https://mlsanity.com/paper/2606.18231/) (2026)

## Abstract (arXiv)

With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.