{"doi":"10.1145/3544548.3581279","title":"WordGesture-GAN: Modeling Word-Gesture Movement with Generative Adversarial Network","abstract":"Word-gesture production models that can synthesize word-gestures are critical to the training and evaluation of word-gesture keyboard decoders. We propose WordGesture-GAN, a conditional generative adversarial network that takes arbitrary text as input to generate realistic word-gesture movements in both spatial (i.e., (x, y) coordinates of touch points) and temporal (i.e., timestamps of touch points) dimensions. WordGesture-GAN introduces a Variational Auto-Encoder to extract and embed variations of user-drawn gestures into a Gaussian distribution which can be sampled to control variation in generated gestures. Our experiments on a dataset with 38k gesture samples show that WordGesture-GAN outperforms existing gesture production models including the minimum jerk model [37] and the style-transfer GAN [31, 32] in generating realistic gestures. Overall, our research demonstrates that the proposed GAN structure can learn variations in user-drawn gestures, and the resulting WordGesture-GAN can generate word-gesture movement and predict the distribution of gestures. WordGesture-GAN can serve as a valuable tool for designing and evaluating gestural input systems.","journal":null,"year":2023,"id":390243,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9479,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":984686,"name":"Dongsheng An","orcid":"0000-0002-6765-2578","position":1,"is_corresponding":false},{"id":805877,"name":"Yan Ma","orcid":"0000-0001-8264-3103","position":2,"is_corresponding":false},{"id":805780,"name":"Wenzhe Cui","orcid":"0000-0001-8968-846X","position":3,"is_corresponding":false},{"id":553855,"name":"Shumin Zhai","orcid":"0000-0003-0752-2090","position":4,"is_corresponding":false},{"id":730394,"name":"Xianfeng Gu","orcid":"0000-0001-8226-5851","position":5,"is_corresponding":false},{"id":553856,"name":"Xiaojun Bi","orcid":"0000-0002-9716-7709","position":6,"is_corresponding":false},{"id":1161552,"name":"Jeremy Chu","orcid":"0000-0002-0173-2153","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T01:18:32.854511Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}