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Technologies to enable safe and effective collaboration and coexistencebetween humans and robots have gained significant importance in the last fewyears. A critical component useful for realizing this collaborative paradigm isthe understanding of human and robot 3D poses using non-invasive systems.Therefore, in this paper, we propose a novel vision-based system leveragingdepth data to accurately establish the 3D locations of skeleton joints.Specifically, we introduce the concept of Pose Nowcasting, denoting thecapability of the proposed system to enhance its current pose estimationaccuracy by jointly learning to forecast future poses. The experimentalevaluation is conducted on two different datasets, providing accurate andreal-time performance and confirming the validity of the proposed method onboth the robotic and human scenarios.

3D Pose Nowcasting: Forecast the Future to Improve the Present