Glow with the Flow: AI-Assisted Creation of Ambient Lightscapes for Music Videos

2026-02-09

AI Summary

Professional-grade lighting design for music videos remains inaccessible to consumer-level creators due to its high skill barrier and time-intensive nature. This work proposes the first AI-assisted lighting generation system tailored for non-expert users, which leverages professional heuristic rules to jointly extract multimodal features from audio and video content and automatically produces editable ambient lighting sequences. By innovatively integrating a human-AI collaborative workflow into consumer-grade creative practice, the approach substantially lowers the entry barrier for high-quality lighting design. In three user studies involving 32 participants, the system’s outputs were consistently rated as viable, high-quality baselines for creative refinement, thereby demonstrating its practical utility and effectiveness in real-world applications.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Computer Vision: Computational Photography, Image & Video Synthesis
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Designed light is an established modality for live performance and music playback. Despite the growing availability of consumer smart lighting, the creation of designed light for music visualization remains limited to professional contexts due to time and skill constraints. To address this, we present an AI-assisted system for generating ambient light sequences for music videos. Informed by professional design heuristics, the system extracts salient features from source video and audio to generate an editable preliminary design of object based ambient light effect. We evaluated the system by comparing its autonomous output against hand-authored designs for three music videos. Findings from responses by 32 participants indicate that the initial output provides a viable baseline for further refinement by human authors. This work demonstrates the utility of AI-assisted workflows in supporting the creation and adoption of designed light beyond professional venues.

Problem

• ambient lightscapes
• music videos
• light design
• AI-assisted creation
• smart lighting

Innovation

• AI-assisted design
• ambient lightscapes
• music visualization
• smart lighting
• feature extraction