论文标题
来自单个图像的肖像神经辐射场
Portrait Neural Radiance Fields from a Single Image
论文作者
论文摘要
我们提出了一种从单个爆头肖像估算神经辐射场（NERF）的方法。虽然Nerf已显示出高质量的视图合成，但它需要静态场景的多个图像，因此对于随意捕获和移动主题来说是不切实际的。在这项工作中，我们建议通过使用轻型舞台肖像数据集的元学习框架来预先对多层感知器（MLP）的重量进行预先建模的体积密度和颜色。为了改善看不见的面孔的概括，我们在3D面可变形模型近似的规范坐标空间中训练MLP。我们使用受控捕获对方法进行定量评估，并证明对真实肖像图像的概括，显示出对最先进的结果的有利结果。
We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. To improve the generalization to unseen faces, we train the MLP in the canonical coordinate space approximated by 3D face morphable models. We quantitatively evaluate the method using controlled captures and demonstrate the generalization to real portrait images, showing favorable results against state-of-the-arts.
