# PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation

arXiv: https://arxiv.org/abs/2509.11092 · HTML: https://mlsanity.com/paper/2509.11092/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control](https://mlsanity.com/paper/2503.03751/) (2025)
- [SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression](https://mlsanity.com/paper/2403.07378/) (2024)
- [DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion](https://mlsanity.com/paper/2411.04928/) (2024)
- [4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency](https://mlsanity.com/paper/2312.17225/) (2023)
- [Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models](https://mlsanity.com/paper/2405.16645/) (2024)
- [Wonderland: Navigating 3D Scenes from a Single Image](https://mlsanity.com/paper/2412.12091/) (2024)
- [Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent](https://mlsanity.com/paper/2411.02265/) (2024)
- [4Real: Towards Photorealistic 4D Scene Generation via Video Diffusion Models](https://mlsanity.com/paper/2406.07472/) (2024)
- [360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model](https://mlsanity.com/paper/2401.06578/) (2024)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion](https://mlsanity.com/paper/2410.24203/) (2024)
- [3D-SceneDreamer: Text-Driven 3D-Consistent Scene Generation](https://mlsanity.com/paper/2403.09439/) (2024)
- [Imagine360: Immersive 360 Video Generation from Perspective Anchor](https://mlsanity.com/paper/2412.03552/) (2024)
- [DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenes](https://mlsanity.com/paper/2412.11100/) (2024)
- [What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?](https://mlsanity.com/paper/2505.22129/) (2025)
- [SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time](https://mlsanity.com/paper/2407.15507/) (2024)
- [VidPanos: Generative Panoramic Videos from Casual Panning Videos](https://mlsanity.com/paper/2410.13832/) (2024)
- [TiP4GEN: Text to Immersive Panorama 4D Scene Generation](https://mlsanity.com/paper/2508.12415/) (2025)
- [PanoDreamer: Optimization-Based Single Image to 360 3D Scene With Diffusion](https://mlsanity.com/paper/2412.04827/) (2024)

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

- [PanoWorld-X: Generating Explorable Panoramic Worlds via Sphere-Aware Video Diffusion](https://mlsanity.com/paper/2509.24997/) (2025)
- [RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation](https://mlsanity.com/paper/2509.16500/) (2025)
- [Video Generation Models: A Survey of Post-Training and Alignment](https://mlsanity.com/paper/2610.00812/) (2026)

## Abstract (arXiv)

Generating high-quality 360deg panoramic videos remains a significant challenge due to the fundamental differences between panoramic and traditional perspective-view projections. While perspective videos rely on a single viewpoint with a limited field of view, panoramic content requires rendering the full surrounding environment, making it difficult for standard video generation models to adapt. Existing solutions often introduce complex architectures or large-scale training, leading to inefficiency and suboptimal results. Motivated by the success of Low-Rank Adaptation (LoRA) in style transfer tasks, we propose treating panoramic video generation as an adaptation problem from perspective views. Through theoretical analysis, we demonstrate that LoRA can effectively model the transformation between these projections when its rank exceeds the degrees of freedom in the task. Our approach efficiently fine-tunes a pretrained video diffusion model using only approximately 1,000 videos while achieving high-quality panoramic generation. Experimental results demonstrate that our method maintains proper projection geometry and surpasses previous state-of-the-art approaches in visual quality, left-right consistency, and motion diversity.