# ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

arXiv: https://arxiv.org/abs/2610.05249 · HTML: https://mlsanity.com/paper/2610.05249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 44 references that are in the map.
- First posted on arXiv 4 Oct 2026.

## Builds on (most cited first, 30 of 35)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Modular Primitives for High-Performance Differentiable Rendering](https://mlsanity.com/paper/2011.03277/) (2020)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [Native and Compact Structured Latents for 3D Generation](https://mlsanity.com/paper/2512.14692/) (2025)
- [Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image](https://mlsanity.com/paper/2002.12212/) (2020)
- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (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)
- [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://mlsanity.com/paper/2308.07391/) (2023)
- [MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation](https://mlsanity.com/paper/2412.03558/) (2024)
- [URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images](https://mlsanity.com/paper/2405.11656/) (2024)
- [CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image](https://mlsanity.com/paper/2502.12894/) (2025)
- [Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model](https://mlsanity.com/paper/2410.13882/) (2024)
- [Automated Creation of Digital Cousins for Robust Policy Learning](https://mlsanity.com/paper/2410.07408/) (2024)
- [Real2Code: Reconstruct Articulated Objects via Code Generation](https://mlsanity.com/paper/2406.08474/) (2024)
- [SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects](https://mlsanity.com/paper/2410.16499/) (2024)
- [ROCA: Robust CAD Model Retrieval and Alignment from a Single Image](https://mlsanity.com/paper/2112.01988/) (2021)
- [OPD: Single-view 3D Openable Part Detection](https://mlsanity.com/paper/2203.16421/) (2022)
- [SAGE: Scalable Agentic 3D Scene Generation for Embodied AI](https://mlsanity.com/paper/2602.10116/) (2026)
- [Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling](https://mlsanity.com/paper/2502.02590/) (2025)
- [Particulate: Feed-Forward 3D Object Articulation](https://mlsanity.com/paper/2512.11798/) (2025)
- [GEOPARD: Geometric Pretraining for Articulation Prediction in 3D Shapes](https://mlsanity.com/paper/2504.02747/) (2025)

## Abstract (arXiv)

Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image tabletop reconstruction methods recover plausible scene geometry but typically represent objects as monolithic rigid bodies, limiting interaction to whole-object rigid motion and precluding executable part-level articulation. Meanwhile, recovering a scene layout consistent with the input view remains challenging because a single observation may admit multiple plausible pose-scale configurations. We present ArticuTable, a single-image 3D tabletop reconstruction framework that recovers both executable part-level articulation and an input-view-consistent scene layout. For object modeling, we introduce generation-robust articulation modeling (GRAM), which combines joint fitting guided by a multimodal large language model with semantic state reasoning to recover reliable joint parameters and valid motion ranges from imperfect monolithic proxy meshes, thereby converting them into executable articulated assets. For scene layout, we introduce progressive semantic-geometric scene registration (PSGSR), which progressively narrows the pose-scale search space under complementary metric, planar, and input-view constraints and resolves orientation ambiguity through structure-aware semantic correspondences, yielding a scene layout consistent with the input view. We further contribute ArticuTable-100, a curated collection of 100 simulation-ready tabletop scenes. Extensive evaluation, including a user study, demonstrates strong performance across visual fidelity, input-view consistency, articulation quality, physical plausibility, and simulation readiness.