论文标题
Selfnerf：来自单眼自动旋转视频的快速培训NERF
SelfNeRF: Fast Training NeRF for Human from Monocular Self-rotating Video
论文作者
论文摘要
在本文中，我们提出了Selfnerf，这是一种有效的神经辐射场基于人类绩效的新型视图合成方法。鉴于人类表演者的单眼自我旋转视频，Selfnerf可以在大约二十分钟内从头开始训练并获得高保真性。最近的一些作品利用了神经辐射场进行动态的人类重建。但是，这些方法中的大多数都需要多视图输入，并且需要数小时的培训，这仍然很难进行实际使用。为了解决这个具有挑战性的问题，我们引入了基于多分辨率哈希编码的表面相关表示，该表示可以极大地提高训练速度和汇总框架间信息。几个不同数据集的广泛实验结果证明了Selfnerf对具有挑战性的单眼视频的有效性和效率。
In this paper, we propose SelfNeRF, an efficient neural radiance field based novel view synthesis method for human performance. Given monocular self-rotating videos of human performers, SelfNeRF can train from scratch and achieve high-fidelity results in about twenty minutes. Some recent works have utilized the neural radiance field for dynamic human reconstruction. However, most of these methods need multi-view inputs and require hours of training, making it still difficult for practical use. To address this challenging problem, we introduce a surface-relative representation based on multi-resolution hash encoding that can greatly improve the training speed and aggregate inter-frame information. Extensive experimental results on several different datasets demonstrate the effectiveness and efficiency of SelfNeRF to challenging monocular videos.
