What is SeedVR?

SeedVR is a diffusion transformer model developed by Nanyang Technological University and ByteDance, designed for high-quality universal video restoration. It introduces a shifted window attention mechanism, using large (64x64) windows and variable-sized windows at boundaries, effectively processing videos of any length and resolution, overcoming the performance limitations of traditional methods at different resolutions. SeedVR combines a causal video variational autoencoder (CVVAE) to reduce computational costs based on temporal and spatial compression while maintaining high reconstruction quality. Through large-scale joint training of images and videos and a multi-stage progressive training strategy, SeedVR excels in various video restoration benchmarks, particularly in perceptual quality, generating restoration videos with realistic details and outperforming existing methods in speed.

Main Features of SeedVR

• Video Restoration: SeedVR can restore low-quality, damaged videos, recovering their details and quality, suitable for various video degradation scenarios such as blur and noise.
• Processing Videos of Any Length and Resolution: It is not limited by video length and resolution, effectively restoring long, high-resolution videos to meet different scenario needs.
• Generating Realistic Details: During the restoration process, it generates realistic details, making the restored videos visually more lifelike and natural.
• Efficient Performance: SeedVR's processing speed is fast, more than twice that of existing diffusion-based video restoration methods, offering good practicality and efficiency.

Technical Principles of SeedVR

• Shifted Window Attention Mechanism: Introduces the Swin-MMDiT shifted window attention mechanism in the diffusion transformer. It uses large (64x64) window attention and supports variable-sized windows near the spatial and temporal boundaries, effectively capturing long-range dependencies and overcoming the limitations of traditional window attention in processing videos of different resolutions.
• Causal Video Variational Autoencoder (CVVAE): Based on temporal and spatial compression factors of 4x and 8x respectively, it significantly reduces the computational cost of video restoration while maintaining high reconstruction quality.
• Large-Scale Joint Training: Joint training on large-scale image and video datasets allows the model to learn rich feature representations, enhancing its generalization ability and restoration effects in different scenarios.
• Multi-Stage Progressive Training Strategy: Gradually increases the length and resolution of training data, accelerating the model's convergence on large-scale datasets and improving training efficiency and model performance.

Project Address of SeedVR

• Project Website: https://iceclear.github.io/projects/seedvr/
• GitHub Repository: https://github.com/SeedVR-CVPR25/SeedVR
• arXiv Technical Paper: https://arxiv.org/pdf/2501.01320v1

Application Scenarios of SeedVR

• Film and TV Restoration and Remastering: High-quality restoration of classic films and TV shows, especially early movies or TV series, restoring their clarity and details, bringing them back to life, and providing a better viewing experience for audiences.
• Video Post-Production: Assists post-production personnel in quickly fixing defects in videos during the post-production process, improving the overall quality of the video, and saving time and costs in post-production.
• Advertising Video Production: Restores and enhances advertising video materials, eliminating flaws in the shooting process, and increasing the attractiveness and dissemination effect of advertisements.
• Social Media Video Optimization: Helps users restore and optimize uploaded videos on social media platforms, improving the clarity and visual quality of the videos.
• Surveillance Video Enhancement: Restores and enhances surveillance videos, improving their clarity and detail representation, aiding in better monitoring and analysis.