VideoGigaGAN: Towards Detail-rich Video Super-Resolution

Abstract

Video super-resolution techniques have demonstrated notable temporal coherence when upsampling videos. Despite their capabilities, these methods often produce blurrier outputs compared to their image-based analogs due to limited generative capacities. This leads us to question whether the triumphs of generative image upsamplers can be replicated in VSR tasks without sacrificing temporal integrity. To tackle this, we introduce VideoGigaGAN, a novel generative VSR model that delivers videos with detailed high-frequency components and maintains temporal consistency. VideoGigaGAN extends the GigaGAN image upsampler by incorporating temporal modules, which, without careful integration, can cause significant temporal distortions. We have pinpointed several critical issues and devised strategies that enhance the temporal stability of the resulting videos significantly. Our tests indicate that VideoGigaGAN surpasses previous VSR methods by producing videos with enhanced detail and stable temporal properties. We confirm VideoGigaGAN's performance by benchmarking it against leading VSR models and demonstrating up to eight times super-resolution on public datasets.

Technique Summary

Our Video Super-Resolution (VSR) framework is constructed using the asymmetric U-Net architecture from the GigaGAN image upsampler. We enhance this model for video by integrating temporal attention layers within the decoder to promote temporal consistency. Further consistency improvements are made by integrating features from a flow-guided propagation module. To minimize aliasing artifacts, we implement an Anti-aliasing block in the encoder’s downsampling stages. Additionally, high-frequency details are preserved through skip connections that bypass the BlurPool process, ensuring rich detail in the decoded video.

Key innovations in VideoGigaGAN

1. Temporal Modules: Incorporating temporal convolutional and attention layers to the GAN architecture to improve the handling of time-related changes in videos.
2. Flow-Guided Feature Propagation: Using optical flow to align features across frames, thereby enhancing temporal consistency.
3. Anti-Aliasing Techniques: Implementing strategies to reduce temporal flickering caused by aliasing effects in upsampling.
4. High-Frequency Shuttle: A method to preserve high-frequency details without introducing flicker, ensuring both clarity and consistency across frames.

VideoGigaGan Collections

Compare VideoGigaGan with Sora

Technical Implementation and Architecture Comparison

VideoGigaGAN:

• Based on the GigaGAN image upsampling technology, VideoGigaGAN addresses video sequences by integrating temporal modules, focusing on enhancing the temporal consistency and detail richness of videos.

• It tackles the issue of temporal flickering with its unique methods, improving the temporal consistency through technical enhancements.

Sora:

• Utilizes a Transformer structure combined with diffusion models for converting textual descriptions into vivid, dynamic videos, focusing on the seamless integration of text and video content.

• Employs advanced generative techniques to ensure high-fidelity and contextually accurate video generation from complex narratives, tackling the challenges of content richness and temporal dynamics.

Goals and Application Scenarios Comparison

VideoGigaGAN:

• Targeted at scenarios requiring high-fidelity video reconstruction, such as post-production in films or high-quality video restoration.

• Aims to resolve issues of temporal consistency in video upsampling and provides delicate visual details through technological means.

Sora:

• Aims to create a system capable of generating videos from text, targeting the transformation of complex text descriptions into dynamic videos, suitable for content creators and the media industry.

• Emphasizes deep understanding of text and innovative video generation technology, applicable across a wide range of uses from entertainment to education and advertising.

Technical Advantages and Challenges

VideoGigaGAN:

• Maintains a high level of temporal consistency in processing video data, but faces challenges with temporal flickering and the need for substantial computational resources.

Sora:

• Capable of intuitively converting text to video, creating content closely linked to the text, but may require optimizations to handle large-scale data and improve generation efficiency.