# DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes

arXiv: https://arxiv.org/abs/2412.11100 · HTML: https://mlsanity.com/paper/2412.11100/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 29 of its 33 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 29 of 29)

- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [VBench: Comprehensive Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2311.17982/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes](https://mlsanity.com/paper/2311.13384/) (2023)
- [WonderWorld: Interactive 3D Scene Generation from a Single Image](https://mlsanity.com/paper/2406.09394/) (2024)
- [Localizing Object-level Shape Variations with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.11306/) (2023)
- [ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models](https://mlsanity.com/paper/2310.07702/) (2023)
- [SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions](https://mlsanity.com/paper/2306.05178/) (2023)
- [GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting](https://mlsanity.com/paper/2402.07207/) (2024)
- [DemoFusion: Democratising High-Resolution Image Generation With No ](https://mlsanity.com/paper/2311.16973/) (2023)
- [DiffCollage: Parallel Generation of Large Content with Diffusion Models](https://mlsanity.com/paper/2303.17076/) (2023)
- [DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling](https://mlsanity.com/paper/2404.03575/) (2024)
- [AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration](https://mlsanity.com/paper/2309.10438/) (2023)
- [360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model](https://mlsanity.com/paper/2401.06578/) (2024)
- [Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis](https://mlsanity.com/paper/2306.08645/) (2023)
- [4K4DGen: Panoramic 4D Generation at 4K Resolution](https://mlsanity.com/paper/2406.13527/) (2024)
- [Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation](https://mlsanity.com/paper/2402.10491/) (2024)
- [Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation](https://mlsanity.com/paper/2203.14668/) (2022)
- [360-Degree Panorama Generation from Few Unregistered NFoV Images](https://mlsanity.com/paper/2308.14686/) (2023)
- [Learned representation-guided diffusion models for large-image generation](https://mlsanity.com/paper/2312.07330/) (2023)
- [Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images](https://mlsanity.com/paper/2308.16582/) (2023)
- [Guided Co-Modulated GAN for 360deg Field of View Extrapolation](https://mlsanity.com/paper/2204.07286/) (2022)
- [VividDream: Generating 3D Scene with Ambient Dynamics](https://mlsanity.com/paper/2405.20334/) (2024)
- [SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time](https://mlsanity.com/paper/2407.15507/) (2024)
- [Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas](https://mlsanity.com/paper/2408.15660/) (2024)
- [MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising](https://mlsanity.com/paper/2312.10899/) (2023)

## Built on by (most cited first, 24 of 24)

- [World Simulation with Video Foundation Models for Physical AI](https://mlsanity.com/paper/2511.00062/) (2025)
- [WonderPlay: Dynamic 3D Scene Generation from a Single Image and Actions](https://mlsanity.com/paper/2505.18151/) (2025)
- [Matrix-3D: Omnidirectional Explorable 3D World Generation](https://mlsanity.com/paper/2508.08086/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [PanoWan: Lifting Diffusion Video Generation Models to 360deg with Latitude/Longitude-aware Mechanisms](https://mlsanity.com/paper/2505.22016/) (2025)
- [Streaming Autoregressive Video Generation via Diagonal Distillation](https://mlsanity.com/paper/2603.09488/) (2026)
- [OmniRoam: World Wandering via Long-Horizon Panoramic Video Generation](https://mlsanity.com/paper/2603.30045/) (2026)
- [CamPVG: Camera-Controlled Panoramic Video Generation with Epipolar-Aware Diffusion](https://mlsanity.com/paper/2509.19979/) (2025)
- [SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling](https://mlsanity.com/paper/2508.17756/) (2025)
- [PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion](https://mlsanity.com/paper/2509.24997/) (2025)
- [360Anything: Geometry-Free Lifting of Images and Videos to 360deg](https://mlsanity.com/paper/2601.16192/) (2026)
- [PanFlow: Decoupled Motion Control for Panoramic Video Generation](https://mlsanity.com/paper/2512.00832/) (2025)
- [PanoWorld: Real-World Panoramic Generation](https://mlsanity.com/paper/2607.09661/) (2026)
- [PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation](https://mlsanity.com/paper/2509.11092/) (2025)
- [What if? Emulative Simulation with World Models for Situated Reasoning](https://mlsanity.com/paper/2603.06445/) (2026)
- [SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE](https://mlsanity.com/paper/2606.32033/) (2026)
- [Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation](https://mlsanity.com/paper/2607.26646/) (2026)
- [A Survey on Text-Driven 360-Degree Panorama Generation](https://mlsanity.com/paper/2502.14799/) (2025)
- [EmoSpace: Immersive Affective Image Generation Guided by Fine-Grained Emotion Prototypes](https://mlsanity.com/paper/2602.11658/) (2026)
- [FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion](https://mlsanity.com/paper/2603.17555/) (2026)
- [Unified Panoramic-Gaussian Representation for Monocular 4D Scene Synthesis](https://mlsanity.com/paper/2607.01663/) (2026)
- [Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation](https://mlsanity.com/paper/2609.01997/) (2026)
- [MUGEN: Interactive Panoramic World Exploration via Camera Control](https://mlsanity.com/paper/2609.38077/) (2026)
- [World Observer: Joint Actor-Observer Generation for Persistent World Modeling](https://mlsanity.com/paper/2610.02162/) (2026)

## Abstract (arXiv)

The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360deg panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dynamic content synthesis. In this work, we propose DynamicScaler, addressing these challenges by enabling spatially scalable and panoramic dynamic scene synthesis that preserves coherence across panoramic scenes of arbitrary size. Specifically, we introduce a Offset Shifting Denoiser, facilitating efficient, synchronous, and coherent denoising panoramic dynamic scenes via a diffusion model with fixed resolution through a seamless rotating Window, which ensures seamless boundary transitions and consistency across the entire panoramic space, accommodating varying resolutions and aspect ratios. Additionally, we employ a Global Motion Guidance mechanism to ensure both local detail fidelity and global motion continuity. Extensive experiments demonstrate our method achieves superior content and motion quality in panoramic scene-level video generation, offering a training-free, efficient, and scalable solution for immersive dynamic scene creation with constant VRAM consumption regardless of the output video resolution. Project page is available at hrefhttps://dynamic-scaler.pages.dev/newhttps://dynamic-scaler.pages.dev/new.