Visual Lyrics: Generating Animated Text for Music Lyric Videos with an Augmented Text Editor

Overview

Visual Lyrics is a proof-of-concept system designed to democratize the creation of animated lyric videos by providing a controlled interface based on an augmented text editor. The system addresses the technical and creative barriers to lyric video production by combining multimodal music analysis with large language model capabilities to generate semantically meaningful animations. The work is grounded in a taxonomy of existing lyric video conventions derived from comprehensive examination of the medium.

Methods and approach

The research involved three primary components: extraction of design principles through taxonomy analysis of existing lyric videos; development of a multimodal music analysis pipeline that leverages LLM natural language understanding and code generation to produce animation specifications; and assembly of a dataset of over 300 code-driven creative text animations to serve as reference material for the LLM-driven synthesis process. The system operates through an augmented text editor interface that abstracts technical complexity while maintaining access to creative animation generation. A user study evaluated the system's efficacy in enabling novice users to produce animated lyric videos.

Key Findings

Visual Lyrics demonstrated effectiveness in enabling novices to generate high-quality animated lyric videos through the augmented text editor interface. User study participants reported high ratings across measures of enjoyment, inspiration, and exploratory engagement with the system. The system successfully synthesized creative animations that maintained semantic alignment with lyrical content while meeting production quality standards. The methodology proved viable as a proof-of-concept, with the underlying animation dataset made available as open source material.

Implications

The system reduces technical barriers to lyric video production by abstracting audio analysis, animation coding, and typography coordination into a single unified interface. By leveraging LLM capabilities for semantic understanding and code generation, the approach enables non-expert users to access production workflows previously requiring specialized expertise across multiple domains. The work establishes that multimodal analysis combined with generative models can produce contextually appropriate creative outputs in music video production.