# Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint

arXiv: https://arxiv.org/abs/2410.15391 · HTML: https://mlsanity.com/paper/2410.15391/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 51 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 30 of 45)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering](https://mlsanity.com/paper/2311.12775/) (2023)
- [GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation](https://mlsanity.com/paper/2403.14621/) (2024)
- [Text-to-3D using Gaussian Splatting](https://mlsanity.com/paper/2309.16585/) (2023)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image](https://mlsanity.com/paper/2403.12013/) (2024)
- [BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion](https://mlsanity.com/paper/2307.10816/) (2023)
- [GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models](https://mlsanity.com/paper/2310.08529/) (2023)
- [LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models](https://mlsanity.com/paper/2305.13655/) (2023)
- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D](https://mlsanity.com/paper/2311.16918/) (2023)
- [Text-to-3D with Classifier Score Distillation](https://mlsanity.com/paper/2310.19415/) (2023)
- [MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis](https://mlsanity.com/paper/2402.05408/) (2024)

## Built on by (most cited first, 21 of 21)

- [Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks](https://mlsanity.com/paper/2510.25760/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [PAT3D: Physics-Augmented Text-to-3D Scene Generation](https://mlsanity.com/paper/2511.21978/) (2025)
- [Graph Canvas for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.00091/) (2024)
- [D2GS: Depth-and-Density Guided Gaussian Splatting for Stable and Accurate Sparse-View Reconstruction](https://mlsanity.com/paper/2510.08566/) (2025)
- [Conditional Panoramic Image Generation via Masked Autoregressive Modeling](https://mlsanity.com/paper/2505.16862/) (2025)
- [GENA3D: Generative Amodal 3D Modeling by Bridging 2D Priors and 3D Coherence](https://mlsanity.com/paper/2511.21945/) (2025)
- [Flash Sculptor: Modular 3D Worlds from Objects](https://mlsanity.com/paper/2504.06178/) (2025)
- [FreeInsert: Disentangled Text-Guided Object Insertion in 3D Gaussian Scene without Spatial Priors](https://mlsanity.com/paper/2505.01322/) (2025)
- [CoCo4D: Comprehensive and Complex 4D Scene Generation](https://mlsanity.com/paper/2506.19798/) (2025)
- [A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation](https://mlsanity.com/paper/2511.19004/) (2025)
- [3D-Layout-R1: Structured Reasoning for Language-Instructed Spatial Editing](https://mlsanity.com/paper/2603.22279/) (2026)
- [AnimateScene: Camera-controllable Animation in Any Scene](https://mlsanity.com/paper/2508.05982/) (2025)
- [TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation](https://mlsanity.com/paper/2512.01204/) (2025)
- [Pano3DComposer: Feed-Forward Compositional 3D Scene Generation from Single Panoramic Image](https://mlsanity.com/paper/2603.05908/) (2026)
- [DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation](https://mlsanity.com/paper/2507.13985/) (2025)
- [DM-CFO: A Diffusion Model for Compositional 3D Tooth Generation with Collision-Free Optimization](https://mlsanity.com/paper/2603.03602/) (2026)
- [Perceive-then-Plan: Layout-as-Policy for Monocular 3D Scene Layout Estimation](https://mlsanity.com/paper/2605.25326/) (2026)
- [Causal Reasoning Elicits Controllable 3D Scene Generation](https://mlsanity.com/paper/2509.15249/) (2025)
- [Drag4D: Align Your Motion with Text-Driven 3D Scene Generation](https://mlsanity.com/paper/2509.21888/) (2025)
- [Inclusive Interactive Collisions for Multi-View Consistent Compositional 3D Generation](https://mlsanity.com/paper/2606.24206/) (2026)

## Abstract (arXiv)

We present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious optimization processes. To address these challenges, our approach leverages 2D layouts as a blueprint to facilitate precise and plausible control over 3D generation. Starting with a 2D layout provided by a user or generated from a text description, we first create a coarse 3D scene using a carefully designed initialization process based on efficient reconstruction models. To enforce coherent global 3D layouts and enhance the quality of instance appearances, we propose a collision-aware layout optimization process followed by instance-wise refinement. Experimental results demonstrate that Layout-Your-3D yields more reasonable and visually appealing compositional 3D assets while significantly reducing the time required for each prompt. Additionally, Layout-Your-3D can be easily applicable to downstream tasks, such as 3D editing and object insertion. Our project page is available at:https://colezwhy.github.io/layoutyour3d/