We introduce a novel approach for monocular novel view synthesis of dynamic
scenes. Existing techniques already show impressive rendering quality but tend
to focus on optimization within a single scene without leveraging prior
knowledge. This limitation has been primarily attributed to the lack of
datasets of dynamic scenes available for training and the diversity of scene
dynamics. Our method FlowIBR circumvents these issues by integrating a neural
image-based rendering method, pre-trained on a large corpus of widely available
static scenes, with a per-scene optimized scene flow field. Utilizing this flow
field, we bend the camera rays to counteract the scene dynamics, thereby
presenting the dynamic scene as if it were static to the rendering network. The
proposed method reduces per-scene optimization time by an order of magnitude,
achieving comparable results to existing methods - all on a single
consumer-grade GPU.

我们引入了一种新颖的方法，用于动态场景的单目新视图合成。通过将场景动态性与预训练的神经图像渲染方法相结合，我们的方法绕过了训练数据和场景动态性多样性的限制，通过优化场景流场来实现将动态场景呈现为静态场景，从而降低了优化时间并实现了与现有方法相当的渲染质量。