3D Pose Nowcasting: Forecast the future to improve the present
Year: 2024
Type: article
Abstract: Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios. • We introduce the novel task of 3D Pose Nowcasting. • Our Pose Nowcasting system is based on both 3D Pose Estimation and Forecasting. • We show that knowledge about pose forecasting improves the accuracy of pose estimation. • We apply the proposed system both to human and robots. • Result on different dataset show state-of-the-art performance and robustness.
Authors: Alessandro Simoni, Francesco Marchetti, Guido Borghi, Federico Becattini, Lorenzo Seidenari, Roberto Vezzani, Alberto Del Bimbo
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,200
OpenAlex ID: W4404553704