# Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling

arXiv: https://arxiv.org/abs/2502.02590 · HTML: https://mlsanity.com/paper/2502.02590/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 69 references that are in the map.
- First posted on arXiv 4 Feb 2025.

## Builds on (most cited first, 30 of 61)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Eureka: Human-Level Reward Design via Coding Large Language Models](https://mlsanity.com/paper/2310.12931/) (2023)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [Evaluating Text-to-Visual Generation with Image-to-Text Generation](https://mlsanity.com/paper/2404.01291/) (2024)
- [Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis](https://mlsanity.com/paper/2111.04276/) (2021)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://mlsanity.com/paper/2310.11441/) (2023)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation](https://mlsanity.com/paper/2311.01455/) (2023)
- [RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D](https://mlsanity.com/paper/2311.16918/) (2023)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition](https://mlsanity.com/paper/2307.14535/) (2023)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI](https://mlsanity.com/paper/2404.09465/) (2024)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes](https://mlsanity.com/paper/1911.10949/) (2019)
- [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://mlsanity.com/paper/2212.01558/) (2022)

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

- [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025)
- [DreamArt: Generating Interactable Articulated Objects from a Single Image](https://mlsanity.com/paper/2507.05763/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion](https://mlsanity.com/paper/2510.25765/) (2025)
- [Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation](https://mlsanity.com/paper/2503.13424/) (2025)
- [ArtiLatent: Realistic Articulated 3D Object Generation via Structured Latents](https://mlsanity.com/paper/2510.21432/) (2025)
- [PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects](https://mlsanity.com/paper/2605.21572/) (2026)
- [Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects](https://mlsanity.com/paper/2511.01294/) (2025)
- [ART: Articulated Reconstruction Transformer](https://mlsanity.com/paper/2512.14671/) (2025)
- [PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World](https://mlsanity.com/paper/2605.05163/) (2026)
- [PAct: Part-Decomposed Single-View Articulated Object Generation](https://mlsanity.com/paper/2602.14965/) (2026)
- [Articulate That Object Part (ATOP): 3D Part Articulation via Text and Motion Personalization](https://mlsanity.com/paper/2502.07278/) (2025)
- [SPARK: Sim-ready Part-level Articulated Reconstruction with VLM Knowledge](https://mlsanity.com/paper/2512.01629/) (2025)
- [One Video, One World: Turning Monocular Video into Physical 4D Scenes](https://mlsanity.com/paper/2606.31388/) (2026)
- [SIMART: Decomposing Monolithic Meshes into Sim-ready Articulated Assets via MLLM](https://mlsanity.com/paper/2603.23386/) (2026)
- [LuciBot: Automated Robot Policy Learning from Generated Videos](https://mlsanity.com/paper/2503.09871/) (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)
- [ArtiWorld: LLM-Driven Articulation of 3D Objects in Scenes](https://mlsanity.com/paper/2511.12977/) (2025)
- [DragMesh: Interactive 3D Generation Made Easy](https://mlsanity.com/paper/2512.06424/) (2025)
- [FreeArtGS: Articulated Gaussian Splatting Under Free-moving Scenario](https://mlsanity.com/paper/2603.22102/) (2026)
- [ViPS: Video-informed Pose Spaces for Auto-Rigged Meshes](https://mlsanity.com/paper/2604.17623/) (2026)
- [DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction](https://mlsanity.com/paper/2509.12430/) (2025)
- [UniArt: Unified 3D Representation for Generating 3D Articulated Objects with Open-Set Articulation](https://mlsanity.com/paper/2511.21887/) (2025)
- [MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins](https://mlsanity.com/paper/2603.12936/) (2026)
- [UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets](https://mlsanity.com/paper/2607.13586/) (2026)
- [ArticFlow: Generative Simulation of Articulated Mechanisms](https://mlsanity.com/paper/2511.17883/) (2025)
- [ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals](https://mlsanity.com/paper/2602.22666/) (2026)
- [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026)
- [Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning](https://mlsanity.com/paper/2606.21938/) (2026)

## Abstract (arXiv)

3D articulated objects modeling has long been a challenging problem, since it requires to capture both accurate surface geometries and semantically meaningful and spatially precise structures, parts, and joints. Existing methods heavily depend on training data from a limited set of handcrafted articulated object categories (e.g., cabinets and drawers), which restricts their ability to model a wide range of articulated objects in an open-vocabulary context. To address these limitations, we propose Articulate Anymesh, an automated framework that is able to convert any rigid 3D mesh into its articulated counterpart in an open-vocabulary manner. Given a 3D mesh, our framework utilizes advanced Vision-Language Models and visual prompting techniques to extract semantic information, allowing for both the segmentation of object parts and the construction of functional joints. Our experiments show that Articulate Anymesh can generate large-scale, high-quality 3D articulated objects, including tools, toys, mechanical devices, and vehicles, significantly expanding the coverage of existing 3D articulated object datasets. Additionally, we show that these generated assets can facilitate the acquisition of new articulated object manipulation skills in simulation, which can then be transferred to a real robotic system. Our Github website is https://articulate-anymesh.github.io.