We present CAT4D, a method for creating 4D (dynamic 3D) scenes from monocular video. CAT4D leverages a multi-view video diffusion model trained on a diverse combination of datasets to enable novel view synthesis at any specified camera poses and timestamps. Combined with a novel sampling approach, this model can transform a single monocular video into a multi-view video, enabling robust 4D reconstruction via optimization of a deformable 3D Gaussian representation. We demonstrate competitive performance on novel view synthesis and dynamic scene reconstruction benchmarks, and highlight the creative capabilities for 4D scene generation from real or generated videos. See our project page for results and interactive demos: \url{cat-4d.github.io}.

本研究针对从单目视频生成4D（动态3D）场景的技术空白，提出了CAT4D方法。该方法利用训练在多样化数据集上的多视角视频扩散模型，结合新颖的采样技术，将单一的单目视频转化为多视角视频，并实现精准的4D重建。这项工作的关键发现是，在新视角合成和动态场景重建基准测试中显示出竞争性表现，并展现了从真实或生成视频中生成4D场景的创造性潜力。

CAT4D：利用多视角视频扩散模型创建四维动态场景