  Photorealistic rendering and reposing of humans is important for enabling
augmented reality experiences. We propose a novel framework to reconstruct the
human and the scene that can be rendered with novel human poses and views from
just a single in-the-wild video. Given a video captured by a moving camera, we
train two NeRF models: a human NeRF model and a scene NeRF model. To train
these models, we rely on existing methods to estimate the rough geometry of the
human and the scene. Those rough geometry estimates allow us to create a
warping field from the observation space to the canonical pose-independent
space, where we train the human model in. Our method is able to learn subject
specific details, including cloth wrinkles and accessories, from just a 10
seconds video clip, and to provide high quality renderings of the human under
novel poses, from novel views, together with the background.
