Music-inspired Automatic Stage Lighting Control (ASLC) has gained increasing attention in recent years due to the substantial time and financial costs associated with hiring and training professional lighting engineers. However, existing methods suffer from several notable limitations: the low interpretability of rule-based approaches, the restriction to single-primary-light control in music-to-color-space methods, and the limited transferability of music-to-controlling-parameter frameworks. To address these gaps, we propose SeqLight, a hierarchical deep learning framework that maps music to multi-light Hue-Saturation-Value (HSV) space. Our approach first customizes SkipBART, an end-to-end single primary light generation model, to predict the full light color distribution for each frame, followed by hybrid Imitation Learning (IL) techniques to derive an effective decomposition strategy that distributes the global color distribution among individual lights. Notably, the light decomposition module can be trained under varying venue-specific lighting configurations using only mixed light data and no professional demonstrations, thereby flexibly adapting across diverse venues. In this stage, we formulate the light decomposition task as a Goal-Conditioned Markov Decision Process (GCMDP), construct an expert demonstration set inspired by Hindsight Experience Replay (HER), and introduce a three-phase IL training pipeline, achieving strong generalization capability. To validate our IL solution for the proposed GCMDP, we conduct a series of quantitative analysis and human study. The code and trained models are provided at https://github.com/RS2002/SeqLight .

翻译：音乐启发的自动舞台灯光控制（ASLC）近年来因聘请和培训专业灯光工程师耗费大量时间和经济成本而受到广泛关注。然而，现有方法存在若干显著局限性：基于规则的方法可解释性低、音乐到色彩空间方法局限于单主灯控制，以及音乐到控制参数框架的可迁移性有限。为填补这些空白，我们提出SeqLight，一种将音乐映射到多灯HSV（色相-饱和度-明度）空间的层次化深度学习框架。该方法首先定制SkipBART——一种端到端的单主灯生成模型，用于预测每一帧的完整灯光色彩分布，随后结合混合模仿学习（IL）技术，推导出将全局色彩分布分配给各灯具的有效分解策略。值得注意的是，该灯光分解模块仅需使用混合灯光数据（无需专业示范）即可在不同场地特有的灯光配置下进行训练，从而灵活适配各类演出场所。在此阶段，我们将灯光分解任务形式化为目标条件马尔可夫决策过程（GCMDP），构建受事后经验回放（HER）启发的专家示范集，并引入三阶段IL训练流程，实现了强大的泛化能力。为验证所提GCMDP的IL解决方案，我们开展了一系列定量分析与人工实验。代码与训练模型已提供于https://github.com/RS2002/SeqLight 。