EasyVolcap: Accelerating Neural Volumetric Video Research
Zhen Xu Tao Xie Sida Peng Haotong Lin Qing Shuai Zhiyuan Yu Guangzhao He Jiaming Sun Hujun Bao Xiaowei Zhou
Volumetric video is a technology that digitally records dynamic events such
as artistic performances, sporting events, and remote conversations. When
acquired, such volumography can be viewed from any viewpoint and timestamp on
flat screens, 3D displays, or VR headsets, enabling immersive viewing
experiences and more flexible content creation in a variety of applications
such as sports broadcasting, video conferencing, gaming, and movie productions.
With the recent advances and fast-growing interest in neural scene
representations for volumetric video, there is an urgent need for a unified
open-source library to streamline the process of volumetric video capturing,
reconstruction, and rendering for both researchers and non-professional users
to develop various algorithms and applications of this emerging technology. In
this paper, we present EasyVolcap, a Python & Pytorch library for accelerating
neural volumetric video research with the goal of unifying the process of
multi-view data processing, 4D scene reconstruction, and efficient dynamic
volumetric video rendering. Our source code is available at
https://github.com/zju3dv/EasyVolcap.