用普通晃动视频生成连贯全景视频，让画面自动延伸。
VidPanos: Generative Panoramic Videos from Casual Panning Videos
- 将全景拼接转为时空补全问题，利用生成模型扩展画面。
- 可处理移动人物、车辆和水流等动态场景，保持时序一致性。
- 适合手机拍摄的自然场景视频，提升沉浸感与视觉体验。
全景图像拼接能提供超出相机视野的宽视角画面。对于静态场景，将摆动视频的帧拼接成全景照片已属成熟技术，但当物体运动时，静态全景无法完整呈现。本文提出一种从随意拍摄的摆动视频生成全景视频的方法，使原始视频仿佛由广角相机录制。我们将全景合成视为时空外推问题，旨在生成与输入视频长度一致的完整全景视频。时空体的一致性补全需要对视频内容和运动有强大而真实的先验知识，因此我们采用生成式视频模型。然而，现有生成模型无法直接适用于全景补全，我们通过将其作为系统组件，充分利用其优势并规避缺陷。实验表明，该方法可成功生成包含人物、车辆、流动水体及静止背景特征的各类真实场景全景视频。
原文摘要 · Abstract (English)
Panoramic image stitching provides a unified, wide-angle view of a scene that extends beyond the camera's field of view. Stitching frames of a panning video into a panoramic photograph is a well-understood problem for stationary scenes, but when objects are moving, a still panorama cannot capture the scene. We present a method for synthesizing a panoramic video from a casually-captured panning video, as if the original video were captured with a wide-angle camera. We pose panorama synthesis as a space-time outpainting problem, where we aim to create a full panoramic video of the same length as the input video. Consistent completion of the space-time volume requires a powerful, realistic prior over video content and motion, for which we adapt generative video models. Existing generative models do not, however, immediately extend to panorama completion, as we show. We instead apply video generation as a component of our panorama synthesis system, and demonstrate how to exploit the strengths of the models while minimizing their limitations. Our system can create video panoramas for a range of in-the-wild scenes including people, vehicles, and flowing water, as well as stationary background features.
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