In this paper, we firstly consider view-dependent effects into single
image-based novel view synthesis (NVS) problems. For this, we propose to
exploit the camera motion priors in NVS to model view-dependent appearance or
effects (VDE) as the negative disparity in the scene. By recognizing
specularities "follow" the camera motion, we infuse VDEs into the input images
by aggregating input pixel colors along the negative depth region of the
epipolar lines. Also, we propose a `relaxed volumetric rendering' approximation
that allows computing the densities in a single pass, improving efficiency for
NVS from single images. Our method can learn single-image NVS from image
sequences only, which is a completely self-supervised learning method, for the
first time requiring neither depth nor camera pose annotations. We present
extensive experiment results and show that our proposed method can learn NVS
with VDEs, outperforming the SOTA single-view NVS methods on the RealEstate10k
and MannequinChallenge datasets.

本文首先考虑了单张基于图像的新视图合成（NVS）问题中的视角相关效果。为此，我们提出利用 NVS 中的相机运动先验来建模视角相关的外观或效果（VDE），即场景中的负视差。通过识别光斑 “跟随” 相机运动，我们通过在极线的负深度区域上聚合输入像素颜色来融入 VDE。同时，我们提出了一种 “放宽的体积渲染” 近似方法，可以在单次遍历中计算密度，提高了从单张图像进行 NVS 的效率。我们的方法只需从图像序列中学习单张图像 NVS，这是一种完全自我监督学习方法，首次无需深度或相机姿态注释。我们展示了广泛的实验证明，我们的方法可以学习具有 VDE 的 NVS，在 RealEstate10k 和 MannequinChallenge 数据集上优于 SOTA 的单视角 NVS 方法。