# PanoWorld: A Generative Spatial World Model for Consistent Whole-House Panorama Synthesis

arXiv: https://arxiv.org/abs/2605.17916 · HTML: https://mlsanity.com/paper/2605.17916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Video-generation world models and simulators, one of 11 directions in World models.
- Builds on 34 of its 42 references that are in the map.
- First posted on arXiv 18 May 2026.

## Placed in

- World models / [Video-generation world models and simulators](https://mlsanity.com/wm/video-generation-world-models-and-simulators/)

## Builds on (most cited first, 30 of 34)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (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)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [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)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model](https://mlsanity.com/paper/2311.06214/) (2023)
- [GS-LRM: Large Reconstruction Model for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.19702/) (2024)
- [MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details](https://mlsanity.com/paper/2507.02546/) (2025)
- [Seedream 4.0: Toward Next-generation Multimodal Image Generation](https://mlsanity.com/paper/2509.20427/) (2025)
- [Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models](https://mlsanity.com/paper/2303.11989/) (2023)
- [TripoSR: Fast 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/2403.02151/) (2024)
- [MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion](https://mlsanity.com/paper/2307.01097/) (2023)
- [HPSv3: Towards Wide-Spectrum Human Preference Score](https://mlsanity.com/paper/2508.03789/) (2025)
- [SceneScape: Text-Driven Consistent Scene Generation](https://mlsanity.com/paper/2302.01133/) (2023)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [Cameras as Relative Positional Encoding](https://mlsanity.com/paper/2507.10496/) (2025)
- [Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs](https://mlsanity.com/paper/2108.08841/) (2021)
- [PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation](https://mlsanity.com/paper/2305.19195/) (2023)
- [PanoDiffusion: 360-degree Panorama Outpainting via Diffusion](https://mlsanity.com/paper/2307.03177/) (2023)
- [Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models](https://mlsanity.com/paper/2311.13141/) (2023)
- [HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds](https://mlsanity.com/paper/2604.14268/) (2026)
- [Mixed Diffusion for 3D Indoor Scene Synthesis](https://mlsanity.com/paper/2405.21066/) (2024)
- [Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation](https://mlsanity.com/paper/2512.16913/) (2025)
- [PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting](https://mlsanity.com/paper/2412.12096/) (2024)
- [Plan2Scene: Converting Floorplans to 3D Scenes](https://mlsanity.com/paper/2106.05375/) (2021)
- [Pano2Room: Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2408.11413/) (2024)
- [OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation](https://mlsanity.com/paper/2603.30045/) (2026)

## Built on by (most cited first, 3 of 3)

- [Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences](https://mlsanity.com/paper/2607.00832/) (2026)
- [STAR: A Spatial-Topology Aware Routing Framework for Generalizable 3D Scene Understanding](https://mlsanity.com/paper/2608.11699/) (2026)
- [PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents](https://mlsanity.com/paper/2610.20240/) (2026)

## Abstract (arXiv)

Generating a consistent whole-house VR tour from a floorplan and style reference requires both photorealistic panoramas and cross-view spatial coherence. Pure 2D generators produce appealing single panoramas but re-imagine geometry and materials when the viewpoint changes, whereas monolithic 3D generation becomes expensive and loses fine texture at multi-room scale. We introduce PanoWorld, a generative spatial world model that treats whole-house synthesis as autoregressive generation of node-based 360-degree panoramas, matching the discrete navigation used by real VR tour products. PanoWorld uses a floorplan-derived 3D shell as a global geometric proxy and a dynamic 3D Gaussian Splatting cache as renderable spatial memory. A feed-forward panoramic LRM designed for metric-scale multi-room 360-degree inputs lifts generated panoramas into local 3DGS updates, while Room-aware Group Attention suppresses cross-room feature interference. A topology-aware progressive caching strategy fuses these local updates without repeatedly reconstructing the full history. By decoupling shell-based geometry guidance from cache-rendered visual memory, PanoWorld preserves high-frequency 2D synthesis quality while improving cross-node layout and material consistency. The project link is https://jjrcn.github.io/PanoWorld-project-home/