# FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis

arXiv: https://arxiv.org/abs/2503.13265 · HTML: https://mlsanity.com/paper/2503.13265/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 72 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 30 of 62)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [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)
- [Structured 3D Latents for Scalable and Versatile 3D Generation](https://mlsanity.com/paper/2412.01506/) (2024)
- [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)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [MotionCtrl: A Unified and Flexible Motion Controller for Video Generation](https://mlsanity.com/paper/2312.03641/) (2023)
- [MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2403.14627/) (2024)
- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)

## Built on by (most cited first, 30 of 37)

- [Video World Models with Long-term Spatial Memory](https://mlsanity.com/paper/2506.05284/) (2025)
- [Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation](https://mlsanity.com/paper/2506.04225/) (2025)
- [Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling](https://mlsanity.com/paper/2507.07982/) (2025)
- [NeoVerse: Enhancing 4D World Model with in-the-wild Monocular Videos](https://mlsanity.com/paper/2601.00393/) (2026)
- [Spatia: Video Generation with Updatable Spatial Memory](https://mlsanity.com/paper/2512.15716/) (2025)
- [VerseCrafter: Dynamic Realistic Video World Model with 4D Geometric Control](https://mlsanity.com/paper/2601.05138/) (2026)
- [WorldExplorer: Towards Generating Fully Navigable 3D Scenes](https://mlsanity.com/paper/2506.01799/) (2025)
- [Positional Encoding Field](https://mlsanity.com/paper/2510.20385/) (2025)
- [Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images](https://mlsanity.com/paper/2511.07222/) (2025)
- [Latent Spatial Memory for Video World Models](https://mlsanity.com/paper/2606.09828/) (2026)
- [Text-to-3D by Stitching a Multi-view Reconstruction Network to a Video Generator](https://mlsanity.com/paper/2510.13454/) (2025)
- [4DWorldBench: A Comprehensive Evaluation Framework for 3D/4D World Generation Models](https://mlsanity.com/paper/2511.19836/) (2025)
- [World Reconstruction From Inconsistent Views](https://mlsanity.com/paper/2603.16736/) (2026)
- [GaussVideoDreamer: 3D Scene Generation with Video Diffusion and Inconsistency-Aware Gaussian Splatting](https://mlsanity.com/paper/2504.10001/) (2025)
- [A Mechanistic View on Video Generation as World Models: State and Dynamics](https://mlsanity.com/paper/2601.17067/) (2026)
- [ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models](https://mlsanity.com/paper/2603.00492/) (2026)
- [AnchoredDream: Zero-Shot 360deg Indoor Scene Generation from a Single View via Geometric Grounding](https://mlsanity.com/paper/2601.16532/) (2026)
- [CamPilot: Improving Camera Control in Video Diffusion Model with Efficient Camera Reward Feedback](https://mlsanity.com/paper/2601.16214/) (2026)
- [Pantheon360: Taming Digital Twin Generation via 3D-Aware 360deg Video Diffusion](https://mlsanity.com/paper/2605.25449/) (2026)
- [GeoStream: Toward Precise Camera Controlled Streaming Video Generation](https://mlsanity.com/paper/2606.15162/) (2026)
- [GeoWorld: Providing Full-frame Geometry Features to Facilitate 3D Scene Generation](https://mlsanity.com/paper/2511.23191/) (2025)
- [Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models](https://mlsanity.com/paper/2604.10578/) (2026)
- [Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale](https://mlsanity.com/paper/2604.11331/) (2026)
- [DreamAnywhere: Object-Centric Panoramic 3D Scene Generation](https://mlsanity.com/paper/2506.20367/) (2025)
- [WorldAgents: Can Foundation Image Models be Agents for 3D World Models?](https://mlsanity.com/paper/2603.19708/) (2026)
- [GTA: Advancing Image-to-3D World Generation via Geometry Then Appearance Video Diffusion](https://mlsanity.com/paper/2605.12957/) (2026)
- [CustomX: Unified Character, Action, and Scene Customization in Video World Models](https://mlsanity.com/paper/2512.17796/) (2025)
- [GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds](https://mlsanity.com/paper/2604.20155/) (2026)
- [Pose-Aware Diffusion for 3D Generation](https://mlsanity.com/paper/2605.00345/) (2026)
- [LoViF 2026 The First Challenge on Holistic Quality Assessment for 4D World Model (PhyScore)](https://mlsanity.com/paper/2605.05187/) (2026)

## Abstract (arXiv)

Generating flexible-view 3D scenes, including 360deg rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework consisting of two key components: (1) a strong video-to-video (V2V) diffusion model to generate high-quality novel view images from incomplete input rendered from a coarse scene, and (2) a progressive expansion process to construct a complete 3D scene. In particular, leveraging an advanced pre-trained video model and accurate depth-estimated training pairs, our V2V model can generate novel views under large camera pose variations. Building upon it, FlexWorld progressively generates new 3D content and integrates it into the global scene through geometry-aware scene fusion. Extensive experiments demonstrate the effectiveness of FlexWorld in generating high-quality novel view videos and flexible-view 3D scenes from single images, achieving superior visual quality under multiple popular metrics and datasets compared to existing state-of-the-art methods. Qualitatively, we highlight that FlexWorld can generate high-fidelity scenes with flexible views like 360deg rotations and zooming. Project page: https://ml-gsai.github.io/FlexWorld.