We introduce a free-viewpoint rendering method -- HumanNeRF -- that works on
a given monocular video of a human performing complex body motions, e.g. a
video from YouTube. Our method enables pausing the video at any frame and
rendering the subject from arbitrary new camera viewpoints or even a full
360-degree camera path for that particular frame and body pose. This task is
particularly challenging, as it requires synthesizing photorealistic details of
the body, as seen from various camera angles that may not exist in the input
video, as well as synthesizing fine details such as cloth folds and facial
appearance. Our method optimizes for a volumetric representation of the person
in a canonical T-pose, in concert with a motion field that maps the estimated
canonical representation to every frame of the video via backward warps. The
motion field is decomposed into skeletal rigid and non-rigid motions, produced
by deep networks. We show significant performance improvements over prior work,
and compelling examples of free-viewpoint renderings from monocular video of
moving humans in challenging uncontrolled capture scenarios.

本文介绍了一种自由视点渲染的方法 - HumanNeRF，它可以在给定的人体复杂运动的单目视频中工作，并且可以使主体在任意新的相机视角或甚至特定帧和身体姿势的全 360 度相机路径下呈现。