论文标题
Neuman：单个视频的神经人类辐射场
NeuMan: Neural Human Radiance Field from a Single Video
论文作者
论文摘要
人类的影照渲染和安息对于实现增强现实体验至关重要。我们提出了一个新颖的框架，以重建人类和场景，可以用新颖的人类姿势和一份野外视频来呈现。给定一个由移动摄像机捕获的视频，我们训练了两个NERF模型：人类NERF模型和一个场景NERF模型。为了训练这些模型，我们依靠现有方法来估计人类和场景的粗糙几何形状。这些粗糙的几何形状估计值使我们能够创建一个从观察空间到独立姿势独立的空间的翘曲场，我们可以在其中训练人类模型。我们的方法能够从仅10秒钟的视频剪辑中学习特定的细节，包括布料皱纹和配件，并从新颖的poses of Novel Poses，以及背景下提供新颖的姿势，以及背景。
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.
