Shoot360: Normal View Video Creation from City Panorama Footage

Rao, Anyi; Xu, Liying; Lin, Dahua

doi:10.1145/3528233.3530702

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Some researchers focus on several key steps, such as frame composition [60], shot selection [23,25], shot cut suggestion [35]. Others tackle high-level automatic procedures with simple user interactions and take multiple videos captured by different cameras to produce a coherent video in different application scenarios [1,24,48] using different data sources [5,31,39,54]. Our system also belongs to high-level automatic creation that takes story/camera scripts as input.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Rao¹,

Jiang²,

Guo³

et al. 2023

Preprint

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Dynamic Storyboard 2 Dynamic Storyboard 1 Virtual Environment 📷 Camera Scripts 2: Dolly Full High-angle 📷 Camera scripts 1: Push Medium Eye-level 📖 Story scripts: Jane and Jack are arguing in living room Figure 1. We present Virtual Dynamic Storyboard (VDS) that takes user input story and camera scripts and automatically composes dynamic storyboards in an engine-based virtual environment for pre-visualization. Here we show two results produced by VDS with top-ranked scores of quality. Video demos can be found in the supplementary.

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Section: Related Workmentioning

confidence: 99%

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Rao¹,

Jiang²,

Guo³

et al. 2023

Preprint

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“…Xiong et al [39] develop a weaklysupervised framework that uses text as input to automatically create video sequences from a shot collection. Some researchers focus on generating various video styles based on various manually defined conditions [2,12,23,24,27,33]. Leaken et al [21] propose a system for efficient video editing by offering a set of basic idioms.…”

Section: Related Workmentioning

confidence: 99%

Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows

Rao¹,

Jiang²,

Wang³

et al. 2022

Preprint

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The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery. But it is hard to figure out the statistical pattern and apply intelligent processing due to the lack of high-quality training data. To solve this issue, we first collect a novel benchmark on this setting with four diverse scenarios including concerts, sports games, gala shows, and contests, where each scenario contains 6 synchronized tracks recorded by different cameras. It contains 88-hour raw videos that contribute to the 14-hour edited videos. Based on this benchmark, we further propose a new approach temporal and contextual transformer that utilizes clues from historical shots and other views to make shot transition decisions and predict which view to be used. Extensive experiments show that our method outperforms existing methods on the proposed multi-camera editing benchmark. 1 * Corresponding author 1 A shot is a series of continuous frames recorded by a camera and a track refers to the video recorded by one camera from a specific view.

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Omnidirectional visual computing: Foundations, challenges, and applications

Silveira

Jung

2023

Computers & Graphics

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Shoot360: Normal View Video Creation from City Panorama Footage

Cited by 3 publications

References 29 publications

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows

Omnidirectional visual computing: Foundations, challenges, and applications

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