# FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion

arXiv: https://arxiv.org/abs/2510.25765 · HTML: https://mlsanity.com/paper/2510.25765/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 64 of its 91 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 64)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [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)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets](https://mlsanity.com/paper/2406.13897/) (2024)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion](https://mlsanity.com/paper/2311.07885/) (2023)
- [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://mlsanity.com/paper/2311.15475/) (2023)
- [Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers](https://mlsanity.com/paper/2312.09147/) (2023)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer](https://mlsanity.com/paper/2405.14832/) (2024)
- [Text-To-4D Dynamic Scene Generation](https://mlsanity.com/paper/2301.11280/) (2023)
- [Category-Level Articulated Object Pose Estimation](https://mlsanity.com/paper/1912.11913/) (2019)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2405.20343/) (2024)
- [Shape2Motion: Joint Analysis of Motion Parts and Attributes from 3D Shapes](https://mlsanity.com/paper/1903.03911/) (2019)
- [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023)
- [Animate124: Animating One Image to 4D Dynamic Scene](https://mlsanity.com/paper/2311.14603/) (2023)
- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)

## Built on by (most cited first, 22 of 22)

- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects](https://mlsanity.com/paper/2605.21572/) (2026)
- [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)
- [SPARK: Sim-ready Part-level Articulated Reconstruction with VLM Knowledge](https://mlsanity.com/paper/2512.01629/) (2025)
- [MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction](https://mlsanity.com/paper/2603.19231/) (2026)
- [MorphAny3D: Unleashing the Power of Structured Latent in 3D Morphing](https://mlsanity.com/paper/2601.00204/) (2026)
- [Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control](https://mlsanity.com/paper/2606.14699/) (2026)
- [DragMesh: Interactive 3D Generation Made Easy](https://mlsanity.com/paper/2512.06424/) (2025)
- [NeuROK: Generative 4D Neural Object Kinematics](https://mlsanity.com/paper/2605.30347/) (2026)
- [MotionAnymesh: Physics-Grounded Articulation for Simulation-Ready Digital Twins](https://mlsanity.com/paper/2603.12936/) (2026)
- [ArticFlow: Generative Simulation of Articulated Mechanisms](https://mlsanity.com/paper/2511.17883/) (2025)
- [EgoFun3D: Modeling Interactive Objects from Egocentric Videos using Function Templates](https://mlsanity.com/paper/2604.11038/) (2026)
- [Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects](https://mlsanity.com/paper/2604.25781/) (2026)
- [Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning](https://mlsanity.com/paper/2606.21938/) (2026)
- [UnfoldArt: Zero-Shot Recovery of Full Articulated 3D Objects from Text or Image](https://mlsanity.com/paper/2606.30608/) (2026)
- [NeoWorld-Pro: Programming Interactive Scenes from Monocular Images for Embodied Simulation](https://mlsanity.com/paper/2608.24212/) (2026)
- [Lookalike3D: Seeing Double in 3D](https://mlsanity.com/paper/2603.24713/) (2026)
- [DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics](https://mlsanity.com/paper/2604.07758/) (2026)
- [PWM-ArtGen: Part World Model for Articulated Object Generation](https://mlsanity.com/paper/2607.02045/) (2026)
- [ArtLang: Structured Language-to-Kinematics Grounding for Articulated 3D Actuation](https://mlsanity.com/paper/2608.15419/) (2026)
- [FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations](https://mlsanity.com/paper/2609.20817/) (2026)

## Abstract (arXiv)

Articulated 3D objects are central to many applications in robotics, AR/VR, and animation. Recent approaches to modeling such objects either rely on optimization-based reconstruction pipelines that require dense-view supervision or on feed-forward generative models that produce coarse geometric approximations and often overlook surface texture. In contrast, open-world 3D generation of static objects has achieved remarkable success, especially with the advent of native 3D diffusion models such as Trellis. However, extending these methods to articulated objects by training native 3D diffusion models poses significant challenges. In this work, we present FreeArt3D, a training-free framework for articulated 3D object generation. Instead of training a new model on limited articulated data, FreeArt3D repurposes a pre-trained static 3D diffusion model (e.g., Trellis) as a powerful shape prior. It extends Score Distillation Sampling (SDS) into the 3D-to-4D domain by treating articulation as an additional generative dimension. Given a few images captured in different articulation states, FreeArt3D jointly optimizes the object's geometry, texture, and articulation parameters without requiring task-specific training or access to large-scale articulated datasets. Our method generates high-fidelity geometry and textures, accurately predicts underlying kinematic structures, and generalizes well across diverse object categories. Despite following a per-instance optimization paradigm, FreeArt3D completes in minutes and significantly outperforms prior state-of-the-art approaches in both quality and versatility. Please check our website for more details: https://czzzzh.github.io/FreeArt3D