VidPanos: Generative Panoramic Videos from Casual Panning Videos

TL;DR

This paper introduces VidPanos, a novel method for synthesizing panoramic videos from casual panning videos by framing it as a space-time outpainting problem, leveraging generative models to produce wide-angle, continuous panoramic scenes.

Contribution

The paper presents a new approach to panoramic video synthesis that adapts generative video models for space-time outpainting, enabling realistic wide-angle videos from casual footage.

Findings

Successfully synthesizes panoramic videos of diverse scenes.

Adapts generative models for consistent space-time completion.

Produces realistic panoramic videos with moving objects and backgrounds.

Abstract

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.