# Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model

arXiv: https://arxiv.org/abs/2410.13882 · HTML: https://mlsanity.com/paper/2410.13882/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation](https://mlsanity.com/paper/2403.03949/) (2024)
- [GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts](https://mlsanity.com/paper/2211.05272/) (2022)
- [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023)
- [URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images](https://mlsanity.com/paper/2405.11656/) (2024)
- [Full-Body Articulated Human-Object Interaction](https://mlsanity.com/paper/2212.10621/) (2022)
- [Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects](https://mlsanity.com/paper/2404.01440/) (2024)
- [Real2Code: Reconstruct Articulated Objects via Code Generation](https://mlsanity.com/paper/2406.08474/) (2024)
- [SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement](https://mlsanity.com/paper/2408.00653/) (2024)
- [CAGE: Controllable Articulation GEneration](https://mlsanity.com/paper/2312.09570/) (2023)
- [Visual Identification of Articulated Object Parts](https://mlsanity.com/paper/2012.00284/) (2020)

## Built on by (most cited first, 30 of 96)

- [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025)
- [PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image](https://mlsanity.com/paper/2511.13648/) (2025)
- [ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting](https://mlsanity.com/paper/2503.08135/) (2025)
- [Articraft: An Agentic System for Scalable Articulated 3D Asset Generation](https://mlsanity.com/paper/2605.15187/) (2026)
- [DRAWER: Digital Reconstruction and Articulation With Environment Realism](https://mlsanity.com/paper/2504.15278/) (2025)
- [PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding](https://mlsanity.com/paper/2510.20155/) (2025)
- [DreamArt: Generating Interactable Articulated Objects from a Single Image](https://mlsanity.com/paper/2507.05763/) (2025)
- [Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels](https://mlsanity.com/paper/2508.17437/) (2025)
- [The Reality Gap in Robotics: Challenges, Solutions, and Best Practices](https://mlsanity.com/paper/2510.20808/) (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)
- [URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model](https://mlsanity.com/paper/2511.00940/) (2025)
- [Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation](https://mlsanity.com/paper/2503.13424/) (2025)
- [iTACO: Interactable Digital Twins of Articulated Objects from Casually Captured RGBD Videos](https://mlsanity.com/paper/2506.08334/) (2025)
- [VideoArtGS: Building Digital Twins of Articulated Objects from Monocular Video](https://mlsanity.com/paper/2509.17647/) (2025)
- [3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code](https://mlsanity.com/paper/2606.01057/) (2026)
- [SplArt: Articulation Estimation and Part-Level Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2506.03594/) (2025)
- [LARM: A Large Articulated-Object Reconstruction Model](https://mlsanity.com/paper/2511.11563/) (2025)
- [PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects](https://mlsanity.com/paper/2605.21572/) (2026)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models](https://mlsanity.com/paper/2606.02580/) (2026)
- [ScrewSplat: An End-to-End Method for Articulated Object Recognition](https://mlsanity.com/paper/2508.02146/) (2025)
- [Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots](https://mlsanity.com/paper/2511.00917/) (2025)
- [Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects](https://mlsanity.com/paper/2511.01294/) (2025)
- [PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World](https://mlsanity.com/paper/2605.05163/) (2026)
- [Guiding Diffusion-Based Articulated Object Generation by Partial Point Cloud Alignment and Physical Plausibility Constraints](https://mlsanity.com/paper/2508.00558/) (2025)
- [PAct: Part-Decomposed Single-View Articulated Object Generation](https://mlsanity.com/paper/2602.14965/) (2026)
- [ArtLLM: Generating Articulated Assets via 3D LLM](https://mlsanity.com/paper/2603.01142/) (2026)
- [FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos](https://mlsanity.com/paper/2604.05621/) (2026)
- [ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation](https://mlsanity.com/paper/2604.10992/) (2026)

## Abstract (arXiv)

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting their broader applications. To overcome this challenge, we present Articulate-Anything, a system that automates the articulation of diverse, complex objects from many input modalities, including text, images, and videos. Articulate-Anything leverages vision-language models (VLMs) to generate code that can be compiled into an interactable digital twin for use in standard 3D simulators. Our system exploits existing 3D asset datasets via a mesh retrieval mechanism, along with an actor-critic system that iteratively proposes, evaluates, and refines solutions for articulating the objects, self-correcting errors to achieve a robust outcome. Qualitative evaluations demonstrate Articulate-Anything's capability to articulate complex and even ambiguous object affordances by leveraging rich grounded inputs. In extensive quantitative experiments on the standard PartNet-Mobility dataset, Articulate-Anything substantially outperforms prior work, increasing the success rate from 8.7-11.6% to 75% and setting a new bar for state-of-the-art performance. We further showcase the utility of our system by generating 3D assets from in-the-wild video inputs, which are then used to train robotic policies for fine-grained manipulation tasks in simulation that go beyond basic pick and place. These policies are then transferred to a real robotic system.