Abstract
Generating high-quality 360° 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.
Abstract (translated)
生成高质量的360°全景视频仍然是一个重大挑战，因为全景和传统透视视图投影之间存在根本差异。传统的视角视频依赖于单一视点且视野有限，而全景内容则需要渲染整个周围环境，这使得标准视频生成模型难以适应这种需求。现有的解决方案通常会引入复杂的架构或大规模训练，导致效率低下并产生次优结果。受低秩适配（LoRA）在风格转移任务中成功的启发，我们提出将全景视频生成视为从透视视角进行转换的问题。通过理论分析，我们证明当LoRA的秩超过任务中的自由度时，它可以有效地建模这两种投影之间的转换。我们的方法可以高效地微调预训练的视频扩散模型，并仅使用大约1,000个视频就能实现高质量全景视频生成。实验结果表明，该方法在保持正确的投影几何的同时，在视觉质量、左右一致性以及运动多样性方面超越了现有的最先进方法。
URL
https://arxiv.org/abs/2509.11092
