用对比学习生成更真实的人类日常活动，让游戏角色行为更自然。
ORACLE: Orchestrate NPC Daily Activities using Contrastive Learning with Transformer-CVAE
- 结合Transformer、CVAE与对比学习，生成连贯的室内活动序列。
- 在CASAS数据集上实现更少重复、更贴近真实人类作息的活动计划。
- 适合做沉浸式虚拟环境、游戏或智能助手的动态行为建模。
将非玩家角色（NPC）融入数字环境，有助于提升用户沉浸感与认知参与度。真实反映人类日常作息的复杂活动编排，对提升数字环境真实性至关重要。然而，传统方法常导致行为单调重复，难以捕捉真实活动模式的细微差别。为此，我们提出ORACLE——一种基于对比学习与Transformer-CVAE的生成模型，用于合成逼真的室内日常活动计划，确保NPC在数字栖息地中的真实存在感。该模型利用CASAS智能家庭数据集中的24小时室内活动序列，解决数据不平衡、训练样本稀少及缺乏预训练活动模式模型等挑战。通过融合Transformer的序列处理能力、条件变分自编码器的可控生成性以及对比学习的判别优化，ORACLE在实验中展现出优于现有方法的活动计划生成能力与设计策略有效性。
原文摘要 · Abstract (English)
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.
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