Human portraits exhibit various appearances when observed from different views under different lighting conditions. We can easily imagine how the face will look like in another setup, but computer algorithms still fail on this problem given limited observations. To this end, we present a system for portrait view synthesis and relighting: given multiple portraits, we use a neural network to predict the light-transport field in 3D space, and from the predicted Neural Light-transport Field (NeLF) produce a portrait from a new camera view under a new environmental lighting. Our system is trained on a large number of synthetic models, and can generalize to different synthetic and real portraits under various lighting conditions. Our method achieves simultaneous view synthesis and relighting given multi-view portraits as the input, and achieves state-of-the-art results.

中文翻译：

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NeLF：用于人像视图合成和重新照明的神经光传输场

在不同光照条件下从不同视角观察时，人物肖像呈现出不同的外观。我们很容易想象面部在另一种设置中的样子，但鉴于有限的观察，计算机算法仍然无法解决这个问题。为此，我们提出了一个用于人像视图合成和重新照明的系统：给定多个人像，我们使用神经网络来预测 3D 空间中的光传输场，并根据预测的神经光传输场 (NeLF) 生成人像从新的环境照明下的新相机视图。我们的系统在大量合成模型上进行了训练，并且可以在各种光照条件下推广到不同的合成和真实人像。我们的方法在给定多视图肖像作为输入的情况下实现了同时视图合成和重新照明，