# MesaTask: Towards Task-Driven Tabletop Scene Generation via 3D Spatial Reasoning

arXiv: https://arxiv.org/abs/2509.22281 · HTML: https://mlsanity.com/paper/2509.22281/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner](https://mlsanity.com/paper/2405.14979/) (2024)
- [MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation](https://mlsanity.com/paper/2412.03558/) (2024)
- [CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image](https://mlsanity.com/paper/2502.12894/) (2025)
- [Automated Creation of Digital Cousins for Robust Policy Learning](https://mlsanity.com/paper/2410.07408/) (2024)
- [I-Design: Personalized LLM Interior Designer](https://mlsanity.com/paper/2404.02838/) (2024)
- [Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation](https://mlsanity.com/paper/2505.02836/) (2025)
- [REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment](https://mlsanity.com/paper/2405.18525/) (2024)
- [LLplace: The 3D Indoor Scene Layout Generation and Editing via Large Language Model](https://mlsanity.com/paper/2406.03866/) (2024)
- ["Set It Up!": Functional Object Arrangement with Compositional Generative Models](https://mlsanity.com/paper/2405.11928/) (2024)
- [ManiTaskGen: A Comprehensive Task Generator for Benchmarking and Improving Vision-Language Agents on Embodied Decision-Making](https://mlsanity.com/paper/2505.20726/) (2025)

## Built on by (most cited first, 16 of 16)

- [SceneReVis: A Self-Reflective Vision-Grounded Framework for 3D Indoor Scene Synthesis via Multi-Turn RL](https://mlsanity.com/paper/2602.09432/) (2026)
- [DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks](https://mlsanity.com/paper/2604.16484/) (2026)
- [ArtiWorld: LLM-Driven Articulation of 3D Objects in Scenes](https://mlsanity.com/paper/2511.12977/) (2025)
- [TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation](https://mlsanity.com/paper/2512.01204/) (2025)
- [STABLE: Simulation-Ready Tabletop Layout Generation via a Semantics-Physics Dual System](https://mlsanity.com/paper/2605.16137/) (2026)
- [Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction](https://mlsanity.com/paper/2608.18840/) (2026)
- [Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling](https://mlsanity.com/paper/2608.30821/) (2026)
- [Pair2Scene: Learning Local Object Relations for Procedural Scene Generation](https://mlsanity.com/paper/2604.11808/) (2026)
- [3D Generation for Embodied AI and Robotic Simulation: A Survey](https://mlsanity.com/paper/2604.26509/) (2026)
- [RoboWits: Unexpected Challenges for Robotic Creative Problem Solving](https://mlsanity.com/paper/2605.30326/) (2026)
- [PhyScene3D: Physically Consistent Interactive 3D Tabletop Scene Generation](https://mlsanity.com/paper/2606.01649/) (2026)
- [GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning](https://mlsanity.com/paper/2609.05927/) (2026)
- [Exploring Spatial Intelligence from a Generative Perspective](https://mlsanity.com/paper/2604.20570/) (2026)
- [Orchestrating Spatial Semantics via a Zone-Graph Paradigm for Intricate Indoor Scene Generation](https://mlsanity.com/paper/2605.02537/) (2026)
- [SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution](https://mlsanity.com/paper/2609.05594/) (2026)
- [ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning](https://mlsanity.com/paper/2610.15734/) (2026)

## Abstract (arXiv)

The ability of robots to interpret human instructions and execute manipulation tasks necessitates the availability of task-relevant tabletop scenes for training. However, traditional methods for creating these scenes rely on time-consuming manual layout design or purely randomized layouts, which are limited in terms of plausibility or alignment with the tasks. In this paper, we formulate a novel task, namely task-oriented tabletop scene generation, which poses significant challenges due to the substantial gap between high-level task instructions and the tabletop scenes. To support research on such a challenging task, we introduce MesaTask-10K, a large-scale dataset comprising approximately 10,700 synthetic tabletop scenes with manually crafted layouts that ensure realistic layouts and intricate inter-object relations. To bridge the gap between tasks and scenes, we propose a Spatial Reasoning Chain that decomposes the generation process into object inference, spatial interrelation reasoning, and scene graph construction for the final 3D layout. We present MesaTask, an LLM-based framework that utilizes this reasoning chain and is further enhanced with DPO algorithms to generate physically plausible tabletop scenes that align well with given task descriptions. Exhaustive experiments demonstrate the superior performance of MesaTask compared to baselines in generating task-conforming tabletop scenes with realistic layouts. Project page is at https://mesatask.github.io/