In video production, inserting B-roll is a widely used technique to enrich the story and make a video more engaging. However, determining the right content and positions of B-roll and actually inserting it within the main footage can be challenging, and novice producers often struggle to get both timing and content right. We present B-Script, a system that supports B-roll video editing via interactive transcripts. B-Script has a built-in recommendation system trained on expert-annotated data, recommending users B-roll position and content. To evaluate the system, we conducted a within-subject user study with 110 participants, and compared three interface variations: a timeline-based editor, a transcript-based editor, and a transcript-based editor with recommendations. Users found it easier and were faster to insert B-roll using the transcript-based interface, and they created more engaging videos when recommendations were provided.
Computational assistance in such creative tasks as video and audio editing has been previously proven effective in a number of situations @cite_19 @cite_17 @cite_29 @cite_18 . Video editing assistants often rely on video metadata such as event segmentation @cite_9 , gaze data @cite_11 , multiple-scene shots @cite_26 , camera settings @cite_10 , semantic annotations and camera motions @cite_12 @cite_28 . @cite_18 develop a language based recommendation system for creating visually enhanced audio podcasts. Existing industry-level systems such as Wibbitz @cite_3 or Wochit @cite_13 help video content creators to tell visual stories, including automated content suggestions. In contrast, our tool allows users to focus on the video content in the transcript during editing via transcript-based editing and recommendations, which directly supports B-roll video editing. For developing B-Script, we build a recommendation system based entirely on the transcript of the video, using expert-annotated data for recommending video edits to users. By integrating B-roll search, and recommendations, into the workflow of B-roll insertion, our approach better supports this video editing process.
@cite_13 @cite_18 @cite_11 @cite_26 @cite_28 @cite_29 @cite_9 @cite_3 @cite_19 @cite_10 @cite_12 @cite_17
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Audio Podcasts have gained popularity because they are a compelling form of storytelling and are easy to consume. However, they are not as easy to produce since resources are invested in the research, recording, and editing process and the average length of an episode is over an hour. Some audio podcasts could benefit from visuals to increase engagement and learning, but manually curating them can be arduous and time-consuming. We introduce a tool for automatically visualizing audio podcasts, currently focused on the genre of travelogues. Our system works by first time-aligning the transcript of a given podcast, using NLP techniques to extract entities and track how interesting or relevant they are throughout the podcast, and then retrieving visual data appropriately to describe them, either through transitions on a map or professionally taken photographs with captions. By automatically creating a visual narrative to accompany a podcast, we hope our tool can provide listeners with a better sense of the podcast's topic. We present an approach that takes multiple videos captured by social cameras---cameras that are carried or worn by members of the group involved in an activity---and produces a coherent "cut" video of the activity. Footage from social cameras contains an intimate, personalized view that reflects the part of an event that was of importance to the camera operator (or wearer). We leverage the insight that social cameras share the focus of attention of the people carrying them. We use this insight to determine where the important "content" in a scene is taking place, and use it in conjunction with cinematographic guidelines to select which cameras to cut to and to determine the timing of those cuts. A trellis graph representation is used to optimize an objective function that maximizes coverage of the important content in the scene, while respecting cinematographic guidelines such as the 180-degree rule and avoiding jump cuts. We demonstrate cuts of the videos in various styles and lengths for a number of scenarios, including sports games, street performances, family activities, and social get-togethers. We evaluate our results through an in-depth analysis of the cuts in the resulting videos and through comparison with videos produced by a professional editor and existing commercial solutions. We present EverTutor, a system that automatically generates interactive tutorials on smartphone from user demonstration. For tutorial authors, it simplifies the tutorial creation. For tutorial users, it provides contextual step-by-step guidance and avoids the frequent context switching between tutorials and users' primary tasks. In order to generate the tutorials automatically, EverTutor records low-level touch events to detect gestures and identify on-screen targets. When a tutorial is browsed, the system uses vision-based techniques to locate the target regions and overlays the corresponding input prompt contextually. It also identifies the correctness of users' interaction to guide the users step by step. We conducted a 6-person user study for creating tutorials and a 12-person user study for browsing tutorials, and we compared EverTutor's interactive tutorials to static and video ones. Study results show that creating tutorials by EverTutor is simpler and faster than producing static and video tutorials. Also, when using the tutorials, the task completion time for interactive tutorials were 3-6 times faster than static and video tutorials regardless of age group. In terms of user preference, 83 of the users chose interactive type as the preferred tutorial type and rated it easiest to follow and easiest to understand. We present a video summarization approach that discovers the story of an egocentric video. Given a long input video, our method selects a short chain of video sub shots depicting the essential events. Inspired by work in text analysis that links news articles over time, we define a random-walk based metric of influence between sub shots that reflects how visual objects contribute to the progression of events. Using this influence metric, we define an objective for the optimal k-subs hot summary. Whereas traditional methods optimize a summary's diversity or representative ness, ours explicitly accounts for how one sub-event "leads to" another-which, critically, captures event connectivity beyond simple object co-occurrence. As a result, our summaries provide a better sense of story. We apply our approach to over 12 hours of daily activity video taken from 23 unique camera wearers, and systematically evaluate its quality compared to multiple baselines with 34 human subjects. We present a demonstration-based system for automatically generating succinct step-by-step visual tutorials of photo manipulations. An author first demonstrates the manipulation using an instrumented version of GIMP that records all changes in interface and application state. From the example recording, our system automatically generates tutorials that illustrate the manipulation using images, text, and annotations. It leverages automated image labeling (recognition of facial features and outdoor scene structures in our implementation) to generate more precise text descriptions of many of the steps in the tutorials. A user study comparing our automatically generated tutorials to hand-designed tutorials and screen-capture video recordings finds that users are 20--44 faster and make 60--95 fewer errors using our tutorials. While our system focuses on tutorial generation, we also present some initial work on generating content-dependent macros that use image recognition to automatically transfer selection operations from the example image used in the demonstration to new target images. While our macros are limited to transferring selection operations we demonstrate automatic transfer of several common retouching techniques including eye recoloring, whitening teeth and sunset enhancement. This article outlines a paradigm shift in media production: the advent of computational media production that will automate the capture, editing, and reuse of video content. By integrating metadata creation and (re)use throughout the media production process, we enable the mass customization of video. This paper introduces an evaluated approach to the automatic generation of video narratives from user generated content gathered in a shared repository. In the context of social events, end-users record video material with their personal cameras and upload the content to a common repository. Video narrative techniques, implemented using Narrative Structure Language (NSL) and ShapeShifting Media, are employed to automatically generate movies recounting the event. Such movies are personalized according to the preferences expressed by each individual end-user, for each individual viewing. This paper describes our prototype narrative system, MyVideos, deployed as a web application, and reports on its evaluation for one specific use case: assembling stories of a school concert by parents, relatives and friends. The evaluations carried out through focus groups, interviews and field trials, in the Netherlands and UK, provided validating results and further insights into this approach. We present an interactive drawing tool that provides automated guidance over model photographs to help people practice traditional drawing-by-observation techniques. The drawing literature describes a number of techniques to support this task and help people gain consciousness of the shapes in a scene and their relationships. We compile these techniques and derive a set of construction lines that we automatically extract from a model photograph. We then display these lines over the model to guide its manual reproduction by the user on the drawing canvas. Finally, we use shape-matching to register the user's sketch with the model guides. We use this registration to provide corrective feedback to the user. Our user studies show that automatically extracted construction lines can help users draw more accurately. Furthermore, users report that guidance and corrective feedback help them better understand how to draw.