VideoGigaGAN: Towards Detail-rich Video Super-Resolution
(2404.12388)Abstract
Video super-resolution (VSR) approaches have shown impressive temporal consistency in upsampled videos. However, these approaches tend to generate blurrier results than their image counterparts as they are limited in their generative capability. This raises a fundamental question: can we extend the success of a generative image upsampler to the VSR task while preserving the temporal consistency? We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency. VideoGigaGAN builds upon a large-scale image upsampler -- GigaGAN. Simply inflating GigaGAN to a video model by adding temporal modules produces severe temporal flickering. We identify several key issues and propose techniques that significantly improve the temporal consistency of upsampled videos. Our experiments show that, unlike previous VSR methods, VideoGigaGAN generates temporally consistent videos with more fine-grained appearance details. We validate the effectiveness of VideoGigaGAN by comparing it with state-of-the-art VSR models on public datasets and showcasing video results with $8\times$ super-resolution.
Overview
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VideoGigaGAN extends GigaGAN to video super-resolution, focusing on maintaining temporal consistency and enhancing detail.
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The paper introduces novel techniques like Recurrent Flow-Guided Feature Propagation and Anti-Aliasing Techniques to improve temporal coherence and reduce artifacts.
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High-Frequency Shuttle is implemented to inject high-frequency details into the video, ensuring enhanced texture while maintaining temporal stability.
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Despite added computational complex, VideoGigaGAN remains efficient, marking significant progress in video super-resolution with potential for future advancements.
VideoGigaGAN: Deep Dive into Detail-rich Video Super-Resolution
Introduction
In the field of video super-resolution (VSR), the overarching challenges are twofold: maintaining temporal consistency across enhanced frames, and generating high-frequency details for visual clarity. Traditional VSR methods, although proficient in achieving temporal consistency, often generate outputs that lack detailed textures and appear blurry. Conversely, generative adversarial networks (GANs) have made substantial advances in image super-resolution by effectively modeling high-resolution distributions, but their application to video introduces issues like temporal flickering.
"VideoGigaGAN: Towards Detail-rich Video Super-Resolution" extends the capabilities of GigaGAN, a state-of-the-art image super-resolution model, to the video domain. The paper identifies and addresses the noticeable gap between maintaining temporal consistency and enhancing detail resolution, a dilemma often observed in conventional VSR approaches.
Technical Approach
Baseline Model
VideoGigaGAN builds upon the GigaGAN architecture, originally designed for image tasks, adapting it to handle video input. Initial attempts to convert the image-focused model into a video model through simple inflation of temporal layers resulted in severe temporal inconsistencies, such as flickering. This highlighted the inadequacy of straightforward temporal extension methods for complex video super-resolution tasks.
Novel Contributions
This research introduces several innovative strategies to adapt and optimize GigaGAN for video enhancement:
- Recurrent Flow-Guided Feature Propagation: An enhanced method for aligning frames temporally using bi-directional recurrent neural networks and backward warping guided by optical flow, improving the temporal coherence of generated videos.
- Anti-Aliasing Techniques: Implementation of anti-aliasing blocks during the downsampling process in GigaGAN's encoder, mitigating aliasing artifacts that contribute to temporal flickering.
- High-Frequency (HF) Shuttle: A novel component that injects high-frequency details directly into the decoder stages of GigaGAN, maintaining detail richness without exacerbating temporal inconsistencies.
Theoretical and Practical Implications
- Addressing Temporal Consistency: The efficacy of the proposed recurrent feature propagation and anti-aliasing strategies in bolstering temporal consistency offers a significant advancement in how GANs can be tailored for time-sensitive tasks.
- Detail Enhancement: The HF shuttle mechanism delineates a new pathway for incorporating detailed textural information in generated videos, pushing the boundaries of detail preservation in VSR.
- Model Scalability: Despite its increased computational complexity, VideoGigaGAN maintains reasonable inference times, showcasing a practical balance between performance and efficiency.
Future Directions
- Handling of Longer Sequences: As the lengths of the videos increase, the performance of the current model setup begins to wane. Extending the robustness of VideoGigaGAN to longer sequences without loss of performance is a critical area for future research.
- Improved Processing of Small Objects: Current limitations in processing fine details such as small text point to the necessity for specialized mechanisms that can handle disparate scale features adeptly.
Conclusion
VideoGigaGAN represents a significant stride in video super-resolution technology. By integrating advanced generative models initially designed for images with novel enhancements tailored to video, this work not only tackles longstanding issues in the VSR domain but also sets the stage for future explorations into more efficient and detailed video enhancement techniques. The insights and methodologies from this research are anticipated to influence subsequent developments in video processing and generative modeling.
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