Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
NerfBridge: Bringing Real-time, Online Neural Radiance Field Training to Robotics
Javier Yu, Jun En Low, Keiko Nagami, Mac Schwager
Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction
Shubham Agrawal, Nikhil Chavan-Dafle, Isaac Kasahara, Selim Engin, Jinwook Huh, Volkan Isler
Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems
Nathan Hughes, Yun Chang, Siyi Hu, Rajat Talak, Rumaisa Abdulhai, Jared Strader, Luca Carlone
Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures
Román Cárdenas, Patricia Arroba, José L. Risco-Martín
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
Lizhen Wang, Xiaochen Zhao, Jingxiang Sun, Yuxiang Zhang, Hongwen Zhang, Tao Yu, Yebin Liu
GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation
Zhenhui Ye, Jinzheng He, Ziyue Jiang, Rongjie Huang, Jiawei Huang, Jinglin Liu, Yi Ren, Xiang Yin, Zejun Ma, Zhou Zhao