系统梳理身体与面部动作生成的资源与技术，助力更真实交互式虚拟形象。
A Survey of Body and Face Motion: Datasets, Performance Evaluation Metrics and Generative Techniques
- 整合身体与面部动作生成的核心方法与表示技术
- 涵盖主流数据集与评估指标，提供全面对比基准
- 适合研究虚拟角色、人机交互与多模态生成的学者参考
身体和面部动作在交流中起着关键作用，传递参与者的重要信息。生成模型与多模态学习的进步使得从语音、对话上下文和视觉线索中生成动作成为可能。然而，由于语言与非语言线索及个体性格特征的复杂互动，生成富有表现力且连贯的身体与面部动态仍具挑战。本综述系统回顾了身体与面部动作生成的研究，涵盖核心概念、表征技术、生成方法、数据集与评估指标。我们指出了提升双人场景中虚拟形象真实感、连贯性与表现力的未来方向。据我们所知，这是首个同时覆盖身体与面部动作的综合性综述。详细资源可访问 https://lownish23csz0010.github.io/mogen/。
原文摘要 · Abstract (English)
Body and face motion play an integral role in communication. They convey crucial information on the participants. Advances in generative modeling and multi-modal learning have enabled motion generation from signals such as speech, conversational context and visual cues. However, generating expressive and coherent face and body dynamics remains challenging due to the complex interplay of verbal / non-verbal cues and individual personality traits. This survey reviews body and face motion generation, covering core concepts, representations techniques, generative approaches, datasets and evaluation metrics. We highlight future directions to enhance the realism, coherence and expressiveness of avatars in dyadic settings. To the best of our knowledge, this work is the first comprehensive review to cover both body and face motion. Detailed resources are listed on https://lownish23csz0010.github.io/mogen/.
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