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Gestura harnesses a pre-trained Large Vision-Language Model (LVLM) to align the highly dynamic and diverse patterns of free-form gestures with high-level semantic concepts. To better capture subtle hand movements across different styles, we introduce a Landmark Processing Module that compensate for LVLMs' lack of fine-grained domain knowledge by embedding anatomical hand priors. Further, a Chain-of-Thought (CoT) reasoning strategy enables step-by-step semantic inference, transforming shallow knowledge into deep semantic understanding and significantly enhancing the model's ability to interpret ambiguous or unconventional gestures. Together, these components allow Gestura to achieve robust and adaptable free-form gesture comprehension. Additionally, we have developed the first open-source dataset for free-form gesture intention reasoning and understanding with over 300,000 annotated QA pairs. Experimental results show that Gestura achieves the accuracy of 84.73% (closed-set) \/ 64.14% (open-set) in the exocentric (third-person) setting and 66.14% (closed-set) \/ 21.71% (open-set) in the egocentric (first-person) setting, achieving approximately\n                    <jats:bold>20% and 40% higher accuracy<\/jats:bold>\n                    on closed-set and open-set tasks, respectively, compared to GestureGPT. Moreover, Gestura achieves over a\n                    <jats:bold>100\u00d7 speedup<\/jats:bold>\n                    in response time (1.6 seconds vs. 227 seconds) on an 8B-sized model deployed on a single NVIDIA A100 40GB GPU, and has been validated through real-device experiments with an edge-cloud collaborative setup, bringing free-form gesture understanding markedly closer to practical, real-world deployment. Both the dataset and code about the project can be accessed at https:\/\/evans-lx.github.io\/Gestura\/.\n                  <\/jats:p>","DOI":"10.1145\/3770709","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Gestura: A LVLM-Powered System Bridging Motion and Semantics for Real-Time Free-Form Gesture Understanding"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5122-0154","authenticated-orcid":false,"given":"Zhuoming","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), Shanghai, China and Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5903-5173","authenticated-orcid":false,"given":"Aitong","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), Shanghai, China and Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0979-9803","authenticated-orcid":false,"given":"Mengxi","family":"Jia","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8991-0701","authenticated-orcid":false,"given":"Yubo","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0949-2801","authenticated-orcid":false,"given":"Tengxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3123-3499","authenticated-orcid":false,"given":"Changzhi","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8774-3725","authenticated-orcid":false,"given":"Dell","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0019-4197","authenticated-orcid":false,"given":"Xuelong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01916"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2024.1401803"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592097"},{"key":"e_1_2_1_4_1","unstructured":"Shuai Bai Keqin Chen Xuejing Liu Jialin Wang Wenbin Ge Sibo Song Kai Dang Peng Wang Shijie Wang Jun Tang Humen Zhong Yuanzhi Zhu Mingkun Yang Zhaohai Li Jianqiang Wan Pengfei Wang Wei Ding Zheren Fu Yiheng Xu Jiabo Ye Xi Zhang Tianbao Xie Zesen Cheng Hang Zhang Zhibo Yang Haiyang Xu and Junyang Lin. 2025. 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