Jie Zhou

Xiongjun Guan, Zhiyu Pan, Jianjiang Feng et al.

Finger pose offers promising opportunities to expand human computer interaction capability of touchscreen devices. Existing finger pose estimation algorithms that can be implemented in portable devices predominantly rely on capacitive images, which are currently limited to estimating pitch and yaw angles and exhibit reduced accuracy when processing large-angle inputs (especially when it is greater than 45 degrees). In this paper, we propose BiFingerPose, a novel bimodal based finger pose estimation algorithm capable of simultaneously and accurately predicting comprehensive finger pose information. A bimodal input is explored, including a capacitive image and a fingerprint patch obtained from the touchscreen with an under-screen fingerprint sensor. Our approach leads to reliable estimation of roll angle, which is not achievable using only a single modality. In addition, the prediction performance of other pose parameters has also been greatly improved. The evaluation of a 12-person user study on continuous and discrete interaction tasks further validated the advantages of our approach. Specifically, BiFingerPose outperforms previous SOTA methods with over 21% improvement in prediction performance, 2.5 times higher task completion efficiency, and 23% better user operation accuracy, demonstrating its practical superiority. Finally, we delineate the application space of finger pose with respect to enhancing authentication security and improving interactive experiences, and develop corresponding prototypes to showcase the interaction potential. Our code will be available at https://github.com/XiongjunGuan/DualFingerPose.

Wenxuan Guo, Zhiyu Pan, Ziheng Xi et al.

Sports analysis and viewing play a pivotal role in the current sports domain, offering significant value not only to coaches and athletes but also to fans and the media. In recent years, the rapid development of virtual reality (VR) and augmented reality (AR) technologies have introduced a new platform for watching games. Visualization of sports competitions in VR/AR represents a revolutionary technology, providing audiences with a novel immersive viewing experience. However, there is still a lack of related research in this area. In this work, we present for the first time a comprehensive system for sports competition analysis and real-time visualization on VR/AR platforms. First, we utilize multiview LiDARs and cameras to collect multimodal game data. Subsequently, we propose a framework for multi-player tracking and pose estimation based on a limited amount of supervised data, which extracts precise player positions and movements from point clouds and images. Moreover, we perform avatar modeling of players to obtain their 3D models. Ultimately, using these 3D player data, we conduct competition analysis and real-time visualization on VR/AR. Extensive quantitative experiments demonstrate the accuracy and robustness of our multi-player tracking and pose estimation framework. The visualization results showcase the immense potential of our sports visualization system on the domain of watching games on VR/AR devices. The multimodal competition dataset we collected and all related code will be released soon.