Yao Feng
Postdoctoral Scholar, Computer Science
All Publications
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Textile suit for anywhere full-body motion capture.
Science advances
2026; 12 (10): eaea2646
Abstract
Wearable technology has shown notable promise for tracking human motion, offering valuable insights for fields ranging from biomechanics to healthcare. Traditional motion capture systems, however, are often bulky and disruptive, making them impractical for daily use. Advances in textile-based sensing offer a promising alternative, enabling seamless integration of air- and sweat-permeable sensors into everyday clothing. Here, a sensorized textile suit designed for unobtrusive full-body motion capture is presented. The suit is capable of accurately tracking complex movements without interfering with routine activities. This wearable, using an individual-customized network of fabric-based sensors, autonomously identifies and monitors movement angles and patterns, providing insights into physical range, activity frequency, and exertion levels. Language models are shown to interpret motion data into descriptive language, enhancing its potential for real-world applications. This sensorized textile suit and corresponding algorithms represent a step forward in accessible, continuous movement monitoring in the form of everyday clothing, opening avenues for studying human behavior and health in natural environments.
View details for DOI 10.1126/sciadv.aea2646
View details for PubMedID 41779855
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ChatHuman: Chatting about 3D Humans with Tools
IEEE COMPUTER SOC. 2025: 8150-8161
View details for DOI 10.1109/CVPR52734.2025.00763
View details for Web of Science ID 001601106700182
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DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models
IEEE COMPUTER SOC. 2025: 10847-10857
View details for DOI 10.1109/CVPR52734.2025.01013
View details for Web of Science ID 001601106700430
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ChatGarment: Garment Estimation, Generation and Editing via Large Language Models
IEEE COMPUTER SOC. 2025: 2924-2934
View details for DOI 10.1109/CVPR52734.2025.00278
View details for Web of Science ID 001562507803032