Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.

翻译：伴随言语的手势是自然高效具身人类交流的重要组成部分。这种共语手势的自动生成是计算机动画领域的长期问题，被认为是电影、游戏、虚拟社交空间及社交机器人交互中的使能技术。人类共语手势运动的特异性和非周期性特征，以及手势所涵盖的交际功能的极大多样性，使得该问题颇具挑战性。近年来，随着人类手势运动数据集规模与数量的持续增长，结合深度学习生成模型在数据可用性提升下的突破进展，手势生成研究备受关注。本综述聚焦共语手势生成研究，特别关注深度生成模型。首先，我们阐明描述人类手势动作的理论及其如何与言语互补。接着，在深入探讨深度学习方法之前，简要讨论基于规则和经典统计的手势合成方法。我们以输入模态的选择作为组织原则，考察从音频、文本及非语言输入生成手势的系统。同时，从规模、多样性、运动质量及采集方法等方面梳理相关训练数据集的演变历程。最后，我们识别出手势生成中的关键研究挑战，包括：数据可用性与质量、类人运动生成、手势在对话互动中与共现言语及环境的关联、手势评估，以及手势合成与应用的集成。我们重点介绍应对这些关键挑战的最新方法及其局限性，并指出未来发展方向。