In this paper, we work to bring telepresence to every desktop. Unlike
commercial systems, personal 3D video conferencing systems must render
high-quality videos while remaining financially and computationally viable for
the average consumer. To this end, we introduce a capturing and rendering
system that only requires 4 consumer-grade RGBD cameras and synthesizes
high-quality free-viewpoint videos of users as well as their environments.
Experimental results show that our system renders high-quality free-viewpoint
videos without using object templates or heavy pre-processing. While not
real-time, our system is fast and does not require per-video optimizations.
Moreover, our system is robust to complex hand gestures and clothing, and it
can generalize to new users. This work provides a strong basis for further
optimization, and it will help bring telepresence to every desk in the near
future. The code and dataset will be made available on our website
this https URL

本文提出了一种基于 4 个消费级 RGBD 相机的多捕捉点的渲染系统，该系统可以将用户及其环境合成高质量的自由视点视频，实验证明该系统能够以快速、鲁棒的方式处理各种复杂的手势和着装，并能够应用于新用户，在不需要每个视频的优化的前提下，使远程出席成为可能。

让遥控操作普及到每个桌子

Bringing Telepresence to Every Desk

4D reconstruction and rendering of human activities is critical for immersive
VR/AR experience.Recent advances still fail to recover fine geometry and
texture results with the level of detail present in the input images from
sparse multi-view RGB cameras. In this paper, we propose NeuralHumanFVV, a
real-time neural human performance capture and rendering system to generate
both high-quality geometry and photo-realistic texture of human activities in
arbitrary novel views. We propose a neural geometry generation scheme with a
hierarchical sampling strategy for real-time implicit geometry inference, as
well as a novel neural blending scheme to generate high resolution (e.g., 1k)
and photo-realistic texture results in the novel views. Furthermore, we adopt
neural normal blending to enhance geometry details and formulate our neural
geometry and texture rendering into a multi-task learning framework. Extensive
experiments demonstrate the effectiveness of our approach to achieve
high-quality geometry and photo-realistic free view-point reconstruction for
challenging human performances.

本文提出了一种名为 NeuralHumanFVV 的实时神经人体表演捕捉和渲染系统，采用分层采样策略以及新颖的神经混合方案，能够在新视角下生成高质量的几何和逼真的贴图结果，实现了高质量的四维重建和逼真的自由视点重建。