In this work, we rethink the approach to video super-resolution by introducing a method based on the Diffusion Posterior Sampling framework, combined with an unconditional video diffusion transformer operating in latent space. The video generation model, a diffusion transformer, functions as a space-time model. We argue that a powerful model, which learns the physics of the real world, can easily handle various kinds of motion patterns as prior knowledge, thus eliminating the need for explicit estimation of optical flows or motion parameters for pixel alignment. Furthermore, a single instance of the proposed video diffusion transformer model can adapt to different sampling conditions without re-training. Empirical results on synthetic and real-world datasets illustrate the feasibility of diffusion-based, alignment-free video super-resolution.

翻译：在本研究中，我们通过引入一种基于扩散后验采样框架的方法，结合在潜空间中运行的无条件视频扩散Transformer，重新思考了视频超分辨率的方法。视频生成模型——扩散Transformer，作为一个时空模型运行。我们认为，一个学习真实世界物理规律的强大模型能够轻松处理各种运动模式作为先验知识，从而无需显式估计光流或运动参数进行像素对齐。此外，所提出的视频扩散Transformer模型的单个实例能够适应不同的采样条件而无需重新训练。在合成数据集和真实世界数据集上的实证结果证明了基于扩散的、无需对齐的视频超分辨率的可行性。