Images play a crucial role in the way they listen to music because they can accentuate the feelings and ideas it expresses. It is customary in the music business to release music accompanied by visualizers, lyric videos, and music videos. Stage performances and visual jockey, real-time modifying and choosing images to match the music, are other ways concerts and festivals emphasize the visualization of music. Every place where music can be played now has some display of music, from concert halls to computer screens. Music videos are an example of a type of music visualization that can be as appreciated by a cultural production as the song, since the visuals make the music more immersive.
Because mixing and matching graphics with music requires a lot of time and resources, music visualization is difficult to develop. For example, music video footage must be sourced, shot, lined up, and trimmed. Every step of the music video design and editing process involves making creative decisions regarding color, angles, transitions, themes, and symbols. Coordinating these creative decisions with the intricately complex components of music is challenging. Video editors must learn to combine songs, melodies and rhythms with moving images at strategic intersections.
Users have to watch a lot of material while making videos, but generative AI models can produce a lot of beautiful content. In this article, they provide two design patterns that can be used to orchestrate movie making and create compelling visual stories within AI-generated video: A transition, the initial design pattern, helps represent a change in a produced shot. A grip, the second design pattern, promotes visual continuity and focus throughout a shot. Users can use these two design strategies to reduce motion artifacts and improve the viewability of AI-generated movies. Researchers at Columbia University and Hugging Face present Generative Disco, a text-to-video technology for interactive music visualization. He was one of the first to investigate problems with human-computer interaction in relation to text-to-video systems and to use generative AI to support music visualization.
The intervals serve as the fundamental building block for producing the short music visualization clips that can be created using his methodology. Users first decide which musical interval they want to display. They then generate start and end notices to parameterize the display for that time period. The system offers a brainstorming space to help users identify prompts with recommendations taken from a large language model (GPT-4) and video editing domain knowledge to enable them to explore various ways an interval can begin. and finish. Users can triangulate between lyrics, graphics, and music using the system’s brainstorming features, which include visual understanding of GPT-4 and the other domain information source. Users select two generations to serve as the start and end images of the interval, and then an image sequence is produced warping these two photos to the beat of the music. They conducted user research (n=12) with twelve video and music professionals to assess the Generative Disco workflow. Their survey revealed that users found the system extremely expressive, enjoyable, and easy to navigate. Video experts were able to interact intimately with many parts of the music while producing images they found practical and engaging.
These are the contributions they made:
• A video production framework that uses time lapse as a basic building block. With timing and pauses that enhance visual emphasis, the produced video can communicate meaning through changes in color, theme, style, and time.
• Technique for multimodal brainstorming and rapid ideation linking letters, sounds, and visual targets within prompts using GPT-4 and domain knowledge.
• Generative Disco, a generative AI system that uses a large language model pipeline and a text-to-image model to aid text-to-video production for music display.
• Research demonstrated how experts could use Generative Disco to prioritize expression over execution. In their conversation, they expand on the use cases for their text-to-video approach beyond just music visualization, and talk about how generative AI is already transforming creative work.
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Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
