The integration of Non-player characters (NPCs) within digital environments has been increasingly recognized for its potential to augment user immersion and cognitive engagement. The sophisticated orchestration of their daily activities, reflecting the nuances of human daily routines, contributes significantly to the realism of digital environments. Nevertheless, conventional approaches often produce monotonous repetition, falling short of capturing the intricacies of real human activity plans. In response to this, we introduce ORACLE, a novel generative model for the synthesis of realistic indoor daily activity plans, ensuring NPCs' authentic presence in digital habitats. Exploiting the CASAS smart home dataset's 24-hour indoor activity sequences, ORACLE addresses challenges in the dataset, including its imbalanced sequential data, the scarcity of training samples, and the absence of pre-trained models encapsulating human daily activity patterns. ORACLE's training leverages the sequential data processing prowess of Transformers, the generative controllability of Conditional Variational Autoencoders (CVAE), and the discriminative refinement of contrastive learning. Our experimental results validate the superiority of generating NPC activity plans and the efficacy of our design strategies over existing methods.

翻译：摘要：非玩家角色（NPC）在数字环境中的集成已被日益认识到其在增强用户沉浸感与认知参与方面的潜力。对其日常活动的精细编排——反映人类日常行为的细微差异——显著提升了数字环境的真实感。然而，传统方法常导致单调重复，难以捕捉真实人类活动计划的复杂性。为此，我们提出ORACLE，一种用于合成逼真室内日常活动计划的新型生成模型，确保NPC在数字栖息地中的真实存在。利用CASAS智能家居数据集中的24小时室内活动序列，ORACLE解决了数据集中的挑战，包括不平衡的序列数据、训练样本稀缺以及缺乏封装人类日常活动模式的预训练模型。ORACLE的训练利用了Transformer的序列数据处理能力、条件变分自编码器（CVAE）的生成可控性以及对比学习的判别细化。我们的实验结果验证了ORACLE在生成NPC活动计划方面的优越性，以及设计策略相较于现有方法的有效性。