Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging es-pecially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand - the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://alanjiang98.github.io/.evrgbhand.github.io/.

The multi-path transmission techniques enable multiple paths to maximize resource usage and increase throughput in transmission, which have been installed over mobile devices in recent years. For video streaming applications, compared to the single-path transmission, the multi-path techniques can establish multiple subflows simultaneously to extend the available bandwidth for streaming high-quality videos in mobile devices. Existing adaptive video streaming systems have difficulty in harnessing multi-path scheduling and balancing the tradeoff between the quality of experience (QoE) and quality of service (QoS) concerns. In this paper, we propose an actor-critic network based on Periodical Experience Replay for Multi-path video streaming (PERM). Specifically, PERM employs two actor modules and a critic module: the two actor modules respectively assign the path usage of each subflow and select bitrates for the next chunk of the video, while the critic module predicts the overall objectives. We conduct trace-driven emulation and real-world testbed experiment to examine the performance of PERM, and results show that PERM outperforms state-of-the-art multi-path and single path streaming systems, with an improvement of 10%- 15% on the QoE and QoS metrics.