Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras
<|reference_start|>Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using Sparse RGB Cameras: In this paper, we present a low-budget and high-authenticity bidirectional telepresence system, Tele-Aloha, targeting peer-to-peer communication scenarios. Compared to previous systems, Tele-Aloha utilizes only four sparse RGB cameras, one consumer-grade GPU, and one autostereoscopic screen to achieve high-resolution (2048x2048), real-time (30 fps), low-latency (less than 150ms) and robust distant communication. As the core of Tele-Aloha, we propose an efficient novel view synthesis algorithm for upper-body. Firstly, we design a cascaded disparity estimator for obtaining a robust geometry cue. Additionally a neural rasterizer via Gaussian Splatting is introduced to project latent features onto target view and to decode them into a reduced resolution. Further, given the high-quality captured data, we leverage weighted blending mechanism to refine the decoded image into the final resolution of 2K. Exploiting world-leading autostereoscopic display and low-latency iris tracking, users are able to experience a strong three-dimensional sense even without any wearable head-mounted display device. Altogether, our telepresence system demonstrates the sense of co-presence in real-life experiments, inspiring the next generation of communication.<|reference_end|>
@article{tu2024tele-aloha:,
    title={Tele-Aloha: A Low-budget and High-authenticity Telepresence System Using
  Sparse RGB Cameras},
    author={Hanzhang Tu, Ruizhi Shao, Xue Dong, Shunyuan Zheng, Hao Zhang, Lili
  Chen, Meili Wang, Wenyu Li, Siyan Ma, Shengping Zhang, Boyao Zhou, Yebin Liu},
    journal={arXiv preprint arXiv:2405.14866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14866},
    primaryClass={cs.CV}
}