# PAct: Part-Decomposed Single-View Articulated Object Generation arXiv: https://arxiv.org/abs/2602.14965 ยท HTML: https://mlsanity.com/paper/2602.14965/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 50 references that are in the map. - First posted on arXiv 16 Feb 2026. ## Builds on (most cited first, 30 of 35) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023) - [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023) - [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) - [Splatter Image: Ultra-Fast Single-View 3D Reconstruction](https://mlsanity.com/paper/2312.13150/) (2023) - [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025) - [Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence](https://mlsanity.com/paper/2305.14334/) (2023) - [Native and Compact Structured Latents for 3D Generation](https://mlsanity.com/paper/2512.14692/) (2025) - [PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction](https://mlsanity.com/paper/2311.12024/) (2023) - [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022) - [Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details](https://mlsanity.com/paper/2506.16504/) (2025) - [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023) - [Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model](https://mlsanity.com/paper/2410.13882/) (2024) - [PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers](https://mlsanity.com/paper/2506.05573/) (2025) - [PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models](https://mlsanity.com/paper/2412.18608/) (2024) - [OmniPart: Part-Aware 3D Generation with Semantic Decoupling and Structural Cohesion](https://mlsanity.com/paper/2507.06165/) (2025) - [Efficient Part-level 3D Object Generation via Dual Volume Packing](https://mlsanity.com/paper/2506.09980/) (2025) - [REACTO: Reconstructing Articulated Objects from a Single Video](https://mlsanity.com/paper/2404.11151/) (2024) - [X-Part: high fidelity and structure coherent shape decomposition](https://mlsanity.com/paper/2509.08643/) (2025) - [Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling](https://mlsanity.com/paper/2502.02590/) (2025) - [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025) - [Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception](https://mlsanity.com/paper/2302.01295/) (2023) - [MeshArt: Generating Articulated Meshes with Structure-Guided Transformers](https://mlsanity.com/paper/2412.11596/) (2024) - [PhysX-Anything: Simulation-Ready Physical 3D Assets from Single Image](https://mlsanity.com/paper/2511.13648/) (2025) - [AutoPartGen: Autogressive 3D Part Generation and Discovery](https://mlsanity.com/paper/2507.13346/) (2025) - [S2O: Static to Openable Enhancement for Articulated 3D Objects](https://mlsanity.com/paper/2409.18896/) (2024) - [ArtFormer: Controllable Generation of Diverse 3D Articulated Objects](https://mlsanity.com/paper/2412.07237/) (2024) - [FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion](https://mlsanity.com/paper/2510.25765/) (2025) - [DIPO: Dual-State Images Controlled Articulated Object Generation Powered by Diverse Data](https://mlsanity.com/paper/2505.20460/) (2025) ## Built on by (most cited first, 8 of 8) - [ArtiCAD: Articulated CAD Assembly Design via Multi-Agent Code Generation](https://mlsanity.com/paper/2604.10992/) (2026) - [DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks](https://mlsanity.com/paper/2604.16484/) (2026) - [Instruct-Particulate: Scaling Feed-Forward 3D Object Articulation with Kinematic Control](https://mlsanity.com/paper/2606.14699/) (2026) - [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026) - [KaiNinja: Extending Native 3D Generators to the Part Level](https://mlsanity.com/paper/2609.15659/) (2026) - [ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image](https://mlsanity.com/paper/2610.05249/) (2026) - [AffordCraft: Scalable Construction of Task-Ready Simulation Assets from Single Images](https://mlsanity.com/paper/2610.06643/) (2026) - [EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation](https://mlsanity.com/paper/2610.07969/) (2026) ## Abstract (arXiv) Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR, where functional part decomposition and kinematic motion are essential. Yet producing high-fidelity articulated assets remains difficult to scale because it requires reliable part decomposition and kinematic rigging. Existing approaches largely fall into two paradigms: optimization-based reconstruction or distillation, which can be accurate but often takes tens of minutes to hours per instance, and inference-time methods that rely on template or part retrieval, producing plausible results that may not match the specific structure and appearance in the input observation. We introduce a part-centric generative framework for articulated object creation that synthesizes part geometry, composition, and articulation under explicit part-aware conditioning. Our representation models an object as a set of movable parts, each encoded by latent tokens augmented with part identity and articulation cues. Conditioned on a single image, the model generates articulated 3D assets that preserve instance-level correspondence while maintaining valid part structure and motion. The resulting approach avoids per-instance optimization, enables fast feed-forward inference, and supports controllable assembly and articulation, which are important for embodied interaction. Experiments on common articulated categories (e.g., drawers and doors) show improved input consistency, part accuracy, and articulation plausibility over optimization-based and retrieval-driven baselines, while substantially reducing inference time.