Visual understanding of the world goes beyond the semantics and flat structure of individual images. In this work, we aim to capture both the 3D structure and dynamics of real-world scenes from monocular real-world videos. Our Dynamic Scene Transformer (DyST) model leverages recent work in neural scene representation to learn a latent decomposition of monocular real-world videos into scene content, per-view scene dynamics, and camera pose. This separation is achieved through a novel co-training scheme on monocular videos and our new synthetic dataset DySO. DyST learns tangible latent representations for dynamic scenes that enable view generation with separate control over the camera and the content of the scene.

翻译：摘要：对世界的视觉理解超越了单一图像的语义与扁平结构。本研究旨在从单目真实世界视频中捕捉三维结构和动态信息。我们的动态场景变换器（Dynamic Scene Transformer, DyST）模型利用神经场景表示的最新研究，学习将单目真实世界视频隐式分解为场景内容、逐视图场景动态及相机位姿，并通过一种新颖的联合训练方案（基于单目视频与新合成数据集DySO）实现分解。DyST为动态场景学习到可解释的隐式表示，从而实现对场景相机和内容的独立控制，生成相应视角。