Automatic gesture generation from speech generally relies on implicit modelling of the nondeterministic speech-gesture relationship and can result in averaged motion lacking defined form. Here, we propose a database-driven approach of selecting gestures based on specific motion characteristics that have been shown to be associated with the speech audio. We extend previous work that identified expressive parameters of gesture motion that can both be predicted from speech and are perceptually important for a good speech-gesture match, such as gesture velocity and finger extension. A perceptual study was performed to evaluate the appropriateness of the gestures selected with our method. We compare our method with two baseline selection methods. The first respects timing, the desired onset and duration of a gesture, but does not match gesture form in other ways. The second baseline additionally disregards the original gesture timing for selecting gestures. The gesture sequences from our method were rated as a significantly better match to the speech than gestures selected by either baseline method.
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
Khoury College of Computer Sciences, Northeastern University（东北大学卡内基计算机科学学院）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Department of Physical Therapy, Movement, and Rehabilitation Sciences, Northeastern University（东北大学物理治疗、运动与康复科学系）
GesGPT: Speech Gesture Synthesis With Text Parsing from ChatGPT
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Engineering Research Center of Mixed Reality and Advanced Display（北京混合现实与先进显示工程研究中心）；Institute of Technology, Beijing, China（北京理工大学）
QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；The Chinese University of Hong Kong（香港中文大学）；Huawei Cloud Computing Technologies Co., Ltd（华为云计算技术有限公司）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
GestureFAR: Streaming Co-Speech Gesture Generation with Flow Autoregression
University of Rochester（罗切斯特大学）；University of Tokyo（东京大学）；University of California Santa Cruz（加州大学圣克鲁兹分校）；University of California Los Angeles（加州大学洛杉矶分校）；Meta
The GENEA Challenge 2026: A Large-Scale Disentangled Evaluation of Speech-Driven Gesture Generation on the Seamless Interaction Dataset
KTH Royal Institute of Technology（瑞典皇家理工学院）；Bielefeld University（比勒费尔德大学）；National Library of Sweden（瑞典国家图书馆）；Electronics and Telecommunications Research Institute (ETRI)（电子通信研究院）；Motorica AB（Motorica AB公司）
MotionQ: Operator-Conditioned Motion Quotients for Cross-Observation WiFi Gesture Recognition
Tianjin University（天津大学）；Guizhou Normal University（贵州师范大学）；University of Science and Technology of China（中国科学技术大学）；Guangzhou University（广州大学）；The University of Electro-Communications（电气通信大学）；Hefei University of Technology（合肥工业大学）
Macau University of Science and Technology（澳门科技大学）；Communication University of Zhejiang（浙江传媒学院）；Zhejiang Lab（之江实验室）；Communication University of China（中国传媒大学）；Zhejiang University（浙江大学）；Zhejiang Institute of Economics and Trade（浙江经贸职业技术学院）
UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；The Chinese University of Hong Kong（香港中文大学）；Huawei Cloud Computing Technologies Co., Ltd（华为云计算技术有限公司）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
SentiAvatar: Towards Expressive and Interactive Digital Humans
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；SentiPulse；College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）
