1. Chapter segmentation and indexing
Der Gehängte im Garten der Venus (MCB-DV-8991) is a dance film by Lutz Gregor, Claudia Feest and Dieter Heitkamp, which premiered on 12.12.1988 in Akademie der Künste in Berlin. Being a dance film, it was chosen because of the specificity of the medium – to annotate the movement, but also the filmic frame in which it unfolds.
Chapter segmentation was one of the first things that was tried out. It came down to marking the scenes with a title, which comes with a timestamp and a mark on the timeline. This proved to be useful when it came to locating specific moments within the video and getting familiar with it. The bookmarking feature was used for this action, as it is separately shown on the timeline and differently in the annotation list.
Marking transitions, subchapters and editing techniques lead to an observation - when annotating with a specific task in mind and while rewatching the video, more and more details come to attention, which consequently deepens the understanding of the video at hand and prompts more annotation tasks. All these annotations were differentiated visually in the list and different layers of information were sorted using comments and a highlighting feature. Annotations highlighting a specific moment in time compared to those annotating a timespan are visually distinct in the annotation tool’s timeline representation.
First outcomes: The Annotation list can give an overview of important moments and make visible the structure of the video. Coming back to specific points in the video is much easier, while naming and describing the contents of the video opens up the pathway to analysis and interpretation.
2. Annotating content, cues, choosing details to focus on, qualities
The second video that was annotated was Turning Solo (MCB-DV-8645), the portrait for Naïma Ferré, in which whirling practice is brought into dialogue with Isabelle Schad’s ongoing movement research. The video was chosen as it is a solo work with only one body to observe and capture, and because of its distinctive trait - a slowly but constantly transformational movement quality – which offered itself to annotation.
The process of annotation with a determined research question added to the quality of observing – the emergence and changes of movement patterns and elements that interact with the details of dance (like props, light and costume) also became important. To capture these complex relations that might be perceived differently by different people, a personal terminology for capturing the choreography emerged and developed during annotation.
The Machine Learning-based feature for pose recognition automatically recognizes basic segments of the human body and draws a coarse skeleton over the body. It’s then possible to select body parts or single joints and assign annotations to them, which proved useful for capturing targeted details within the choreography.
First outcomes: Dance and physical practices contain a lot of information which might not be apparent at first view. Deciding on a central research question while annotating can lead to a more focused annotation process. From there, each person can find their own way of deciding which moments are worth capturing and how to connect the annotations. The tool also revealed a knowledge repository potential, where people with diverse information specifically related to the videos can share it with other users, thus enriching the archive.
3. Voice annotation, categorising and filtering annotations
Turning Solo 2 (MCB-DV-12269) is a continuation of the work Turning Solo, with two dancers interpreting the same choreographic material. This video was annotated experimentally during the first watch, using a voice recording feature throughout the whole duration of the piece, which was then transcribed, processed and converted into an annotation.
The approach to annotation shifts depending on the medium – writing text calls for a more structured analysis, while audio recording allows more spontaneous observations. The recorded audio can be saved as annotation and played over the video, but the transcript of it can be best seen as a thought mediation tool between speech and writing.
After editing the notes, a need to categorize them came up, and a “hashtag system” was applied, where custom categories were created and marked with a symbol # for easier searchability. It can be helpful in locating the large number of annotations and categories in the list. Filtering of the annotations is possible by keywords, annotation type (highlights/comments), media type (image/video/text/audio) or analysis type (people recognition/speech to text).
Furthermore, for the purpose of highlighting a single person in group or ensemble pieces, the Machine Learning-based feature for people detection automatically identifies bodies in the video image and draws boxes around them. It’s then possible to select these boxes and assign annotations to a specific box/person.
First outcomes: The hashtag categorization system is just one of many possibilities for structure and recall. There is a lot of creative room to apply such methods according to the annotation objectives. Audio recordings turned out to be useful to gather initial thoughts, and then develop further reflective annotation stemming from there, may it be textual or not. A possible future archival use could be that choreographers (or any other artist involved) watch their own videos and record audio reflections or discuss the concepts they worked with and share insights into their creative process.
4. Referencing external sources, subtitles
The material for this use case was a recording of an event that was part of the series Zwischenrufe/Interventions (MCB-DV-3523), which is centred around dialogues on stage between the body and language, where artists Yvonne Rainer and Xavier Le Roy met to discuss and exchange current concepts and projects. This video was chosen to try out how annotation can be of use for more discursive formats, with an aim to link references outside the video recording itself and build connections between them.
The process began with transcribing the audio by using the automated speech-to-text feature, which generated subtitles and enabled automatically transcribed annotations of interesting quotes.
The annotation list was used for referencing people, places and objects at the times that were mentioned within the recording, as well as for marking key points of conversation. At the time there was no singular database which could provide for the references, therefore links to information that is easily searchable on the internet were added.
First outcomes: An annotation list can be used as a resource for providing further context and other relevant references about the artistic work or points of a discussion. By inviting a person who was somehow involved in the video at hand, annotations can be used to add background information that is not evident from the video alone. When it comes to information sources and the extensiveness of the references, the implementation of Motion Bank Systems would include making sources from the Media Library for Dance and Theatre catalogue easy to find directly from within the tool. A question emerged when it comes to annotating information that is not bound to a specific time within the video but to the video content in general, and it’s something Motion Bank is currently working on.
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