​New video repair technology SeedVR: blur to high definition, can process videos of any length

In the era of rapid development of digital media, the improvement and restoration of video quality has become a topic of great concern. The popularity of video content production has led to increasing demands for video quality. However, many videos are often affected by various factors during the generation or transmission process, leading to problems such as blurry images and lack of details. In order to solve this problem, the research team of Nanyang Technological University and ByteDance recently launched an innovative video recovery technology called SeedVR.

SeedVR uses a cutting-edge Diffusion Transformer model designed to address various challenges faced in video restoration in the real world. Traditional video restoration methods often fail when faced with different resolutions and video lengths. SeedVR uses a moving window attention mechanism to effectively improve the processing capabilities of long video sequences. This design allows the system to use variable-sized windows at the boundaries of the spatial and temporal dimensions, thereby breaking through the limitations of traditional methods when processing high-resolution video. Simply put, a major advantage of SeedVR is that it can process videos of any length and can also fix the flickering problem of AI-generated videos.

In the technical implementation of SeedVR, the research team adopted a basic model called MM-DiT. Compared with the previous self-attention mechanism, SeedVR replaced it with a window attention mechanism and made bold innovations in the window size. Specifically, SeedVR uses a window size of 64x64 instead of the traditional 8x8, which allows it to provide clearer and more detailed restoration effects when processing high-resolution videos.

In addition to the window attention mechanism, SeedVR also integrates a variety of modern technical means to improve video recovery quality. Among them, the use of causal video autoencoders enables the model to better understand and generate video content. In addition, the mixed image and video training method and step-by-step training strategy also provide SeedVR with powerful learning capabilities, allowing it to perform well in both synthetic and real video scenes.

Across multiple benchmarks, SeedVR demonstrated superior performance, especially when processing AI-generated videos. The research team’s experimental results show that SeedVR can effectively maintain the overall consistency of the picture while restoring video details, providing users with a more realistic visual experience.

With the advent of SeedVR, the future of video recovery technology seems even brighter. This innovative technology not only provides higher quality assurance for video creators and consumers, but also opens up new possibilities for applications in related industries. It is worth noting that the SeedVR code has not been released yet.

Project introduction: https://iceclear.github.io/projects/seedvr/