# HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation

arXiv: https://arxiv.org/abs/2504.21650 · HTML: https://mlsanity.com/paper/2504.21650/

## Key facts

- Cited by 10 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 7 of its 8 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## 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, 7 of 7)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [ShareGPT4Video: Improving Video Understanding and Generation with Better Captions](https://mlsanity.com/paper/2406.04325/) (2024)
- [ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation](https://mlsanity.com/paper/2406.18522/) (2024)
- [NOFA: NeRF-based One-shot Facial Avatar Reconstruction](https://mlsanity.com/paper/2307.03441/) (2023)
- [LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation](https://mlsanity.com/paper/2408.13252/) (2024)
- [Text2Light: Zero-Shot Text-Driven HDR Panorama Generation](https://mlsanity.com/paper/2209.09898/) (2022)
- [ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images](https://mlsanity.com/paper/2404.16825/) (2024)

## Built on by (most cited first, 10 of 10)

- [Controllable Video Generation: A Survey](https://mlsanity.com/paper/2507.16869/) (2025)
- [PanoWan: Lifting Diffusion Video Generation Models to 360deg with Latitude/Longitude-aware Mechanisms](https://mlsanity.com/paper/2505.22016/) (2025)
- [AnimaMimic: Imitating 3D Animation from Video Priors](https://mlsanity.com/paper/2512.14133/) (2025)
- [PanFlow: Decoupled Motion Control for Panoramic Video Generation](https://mlsanity.com/paper/2512.00832/) (2025)
- [MATRIX: Mask Track Alignment for Interaction-aware Video Generation](https://mlsanity.com/paper/2510.07310/) (2025)
- [Light4D: Training-Free Extreme Viewpoint 4D Video Relighting](https://mlsanity.com/paper/2602.11769/) (2026)
- [EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation](https://mlsanity.com/paper/2605.22051/) (2026)
- [NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding](https://mlsanity.com/paper/2509.24441/) (2025)
- [TeDiO: Temporal Diagonal Optimization for Training-Free Coherent Video Diffusion](https://mlsanity.com/paper/2605.14136/) (2026)
- [PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents](https://mlsanity.com/paper/2610.20240/) (2026)

## Abstract (arXiv)

The rapid advancement of diffusion models holds the promise of revolutionizing the application of VR and AR technologies, which typically require scene-level 4D assets for user experience. Nonetheless, existing diffusion models predominantly concentrate on modeling static 3D scenes or object-level dynamics, constraining their capacity to provide truly immersive experiences. To address this issue, we propose HoloTime, a framework that integrates video diffusion models to generate panoramic videos from a single prompt or reference image, along with a 360-degree 4D scene reconstruction method that seamlessly transforms the generated panoramic video into 4D assets, enabling a fully immersive 4D experience for users. Specifically, to tame video diffusion models for generating high-fidelity panoramic videos, we introduce the 360World dataset, the first comprehensive collection of panoramic videos suitable for downstream 4D scene reconstruction tasks. With this curated dataset, we propose Panoramic Animator, a two-stage image-to-video diffusion model that can convert panoramic images into high-quality panoramic videos. Following this, we present Panoramic Space-Time Reconstruction, which leverages a space-time depth estimation method to transform the generated panoramic videos into 4D point clouds, enabling the optimization of a holistic 4D Gaussian Splatting representation to reconstruct spatially and temporally consistent 4D scenes. To validate the efficacy of our method, we conducted a comparative analysis with existing approaches, revealing its superiority in both panoramic video generation and 4D scene reconstruction. This demonstrates our method's capability to create more engaging and realistic immersive environments, thereby enhancing user experiences in VR and AR applications.