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。此类共语手势的自动生成是计算机动画领域的长期难题，被认为是电影、游戏、虚拟社交空间以及与社交机器人交互中的关键支撑技术。人类共语手势运动具有特异性和非周期性的特点，且手势涵盖的交际功能极为多样，这些问题共同构成了该领域的核心挑战。近年来，随着人类手势运动数据集规模与数量的持续增长，以及基于深度学习的生成模型在数据可用性提升推动下取得突破，手势生成研究迎来新的热潮。本综述聚焦共语手势生成研究，尤其侧重深度生成模型。首先，阐述描述人类手势动作的理论及其如何补充言语信息。随后，简要讨论基于规则和经典统计的手势合成方法，并深入探讨深度学习方法。我们以输入模态选择为组织原则，系统分析基于音频、文本及非语言输入的手势生成系统，同时梳理相关训练数据集在规模、多样性、运动质量及采集方法上的演变历程。最后，明确手势生成的关键研究挑战，包括数据可用性与质量、生成类人运动、手势与对话中他人言语及环境的关联、手势评估方法，以及手势合成技术的应用集成。本文着重介绍应对各项关键挑战的最新方法及其局限性，并展望未来发展方向。