VidPanos: Generative Panoramic Videos from Casual Panning Videos (2410.13832v2)

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.

• The paper introduces a method that uses generative video models to convert casual panning footage into seamless dynamic panoramas.
• It leverages space-time outpainting to handle moving objects, overcoming the limitations of traditional static panorama synthesis.
• The work demonstrates robust numerical results and provides a new dataset for benchmarking dynamic, immersive video synthesis.

Generative Panoramic Video Synthesis: An Expert Overview

The paper "VidPanos: Generative Panoramic Videos from Casual Panning Videos" addresses a longstanding challenge in computer vision: creating panoramic videos from handheld panning videos, particularly when moving objects are involved. Traditional methods are adept at stitching together static scenes to create panoramic images, yet dynamic scenes with moving objects have remained problematic. This research introduces a novel approach to overcoming these limitations by leveraging generative video models.

Core Contributions

This work introduces a method for converting casual panning videos into seamless, dynamic panoramas. Unlike previous methods restricted to static backgrounds, this approach synthesizes complete panoramic videos that gracefully depict moving elements such as people, vehicles, and water. The method is structured around space-time outpainting, employing sophisticated generative video models to fill in unrecorded parts of a scene.

One of the key challenges the paper addresses is adapting existing generative video models, which are not inherently designed for panoramic synthesis. By integrating video generation as part of the panorama creation system, the method enhances the models' strengths while mitigating their limitations.

Numerical Results and Comparative Analysis

The research demonstrates robust numerical results, showing the system's ability to generate high-quality panoramic videos for a variety of complex scenes. Importantly, the work is accompanied by a new dataset of video panoramas derived from 360-degree videos, providing a benchmark for future research.

Implications and Future Developments

The methodological advancements proposed in this paper have significant implications for both theoretical and practical applications. Theoretically, the integration of generative models with panoramic synthesis workflows enriches our understanding of video completion and synthesis in the presence of dynamic elements. Practically, these developments open new avenues for creating immersive media experiences, enhancing applications in virtual reality, video editing, and interactive media.

Looking ahead, this research suggests promising directions for further exploration. Enhancing the models' capacity to handle even more extensive spatial and temporal contexts could lead to more sophisticated video synthesis capabilities. Additionally, as generative models continue to evolve, integrating these advancements could continue to improve the fidelity and realism of synthesized panoramic videos.

In conclusion, this paper makes a substantial contribution to the field of computer vision, specifically within the field of video synthesis and completion, by effectively using generative video models to create dynamic and coherent panoramic videos from casual footage.