arXiv Open Access 2023

Generative Disco: Text-to-Video Generation for Music Visualization

Vivian Liu Tao Long Nathan Raw Lydia Chilton
Lihat Sumber

Abstrak

Visuals can enhance our experience of music, owing to the way they can amplify the emotions and messages conveyed within it. However, creating music visualization is a complex, time-consuming, and resource-intensive process. We introduce Generative Disco, a generative AI system that helps generate music visualizations with large language models and text-to-video generation. The system helps users visualize music in intervals by finding prompts to describe the images that intervals start and end on and interpolating between them to the beat of the music. We introduce design patterns for improving these generated videos: transitions, which express shifts in color, time, subject, or style, and holds, which help focus the video on subjects. A study with professionals showed that transitions and holds were a highly expressive framework that enabled them to build coherent visual narratives. We conclude on the generalizability of these patterns and the potential of generated video for creative professionals.

Topik & Kata Kunci

Penulis (4)

V

Vivian Liu

T

Tao Long

N

Nathan Raw

L

Lydia Chilton

Format Sitasi

Liu, V., Long, T., Raw, N., Chilton, L. (2023). Generative Disco: Text-to-Video Generation for Music Visualization. https://arxiv.org/abs/2304.08551

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓