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.

翻译：我们提出了一种用于动态场景单目新视角合成的新方法。现有技术已展现出令人印象深刻的渲染质量，但往往侧重于单个场景内的优化，而未利用先验知识。这一局限性主要归因于可用于训练的动态场景数据集缺乏以及场景动态的多样性。我们的方法FlowIBR通过将预训练于大规模可用静态场景语料库的神经图像渲染方法与逐场景优化的场景流场相结合，规避了这些问题。利用该流场，我们对相机光线进行弯曲以抵消场景动态，从而将动态场景呈现为静态场景供渲染网络处理。所提方法将逐场景优化时间降低了一个数量级，并在单个消费级GPU上取得了与现有方法相当的结果。