Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution (2312.00853v2)

Abstract: Real-world low-resolution (LR) videos have diverse and complex degradations, imposing great challenges on video super-resolution (VSR) algorithms to reproduce their high-resolution (HR) counterparts with high quality. Recently, the diffusion models have shown compelling performance in generating realistic details for image restoration tasks. However, the diffusion process has randomness, making it hard to control the contents of restored images. This issue becomes more serious when applying diffusion models to VSR tasks because temporal consistency is crucial to the perceptual quality of videos. In this paper, we propose an effective real-world VSR algorithm by leveraging the strength of pre-trained latent diffusion models. To ensure the content consistency among adjacent frames, we exploit the temporal dynamics in LR videos to guide the diffusion process by optimizing the latent sampling path with a motion-guided loss, ensuring that the generated HR video maintains a coherent and continuous visual flow. To further mitigate the discontinuity of generated details, we insert temporal module to the decoder and fine-tune it with an innovative sequence-oriented loss. The proposed motion-guided latent diffusion (MGLD) based VSR algorithm achieves significantly better perceptual quality than state-of-the-arts on real-world VSR benchmark datasets, validating the effectiveness of the proposed model design and training strategies.

Motion-Guided Latent Diffusion for Real-world Video Super-resolution

The paper introduces a video super-resolution (VSR) methodology focused on addressing the challenges inherent in real-world low-resolution video enhancement. The method, termed Motion-Guided Latent Diffusion (MGLD), leverages modern latent diffusion models to ensure temporal consistency while enhancing perceptual quality in videos with complex and varying degradations.

Core Contributions

The proposed MGLD framework is structured around two major innovations: motion-guided diffusion sampling and a temporal-aware sequence decoder. The overall aim is to mitigate inconsistencies in temporal coherence that arise when using diffusion models for video tasks, by integrating strategies that promote both dense and naturalistic detail regeneration across video sequences.

1. Motion-Guided Diffusion Sampling: The paper proposes a guiding mechanism within the diffusion sampling process to incorporate dynamic motion information from the input video sequence. This targeted guidance employs motion vectors, obtained from optical flow estimates, to align latent features of adjacent frames, thus significantly reducing temporal inconsistencies typical with traditional diffusion approaches. The authors utilize a motion-guided loss, which optimizes the sampling path toward minimizing warping errors between temporally adjacent latent representations.
2. Temporal-aware Sequence Decoder: MGLD further includes a novel fine-tuning approach for the sequence decoder by embedding temporal modeling modules. These modules are designed to specifically address the discontinuities in fine detail generation, which are often a byproduct of relying on pre-trained models. The decoder is adjusted using specialized sequence-oriented losses that incorporate temporal context, thereby enhancing the flow and coherence of generated video frames.

Numerical Results and Impact

Quantitative evaluations reflect the superior performance of MGLD when tested against state-of-the-art VSR models. The paper reports a notable improvement in perceptual quality metrics such as LPIPS and DISTS across several benchmarking datasets. Notably, in real-world applications showcased using the VideoLQ dataset, MGLD outperforms other methods in no-reference quality metrics, demonstrating its practical viability in real video scenarios. Importantly, this improvement does not come at the expense of substantial increases in computational cost, suggesting that the method provides a balanced approach to complex video super-resolution challenges.

Theoretical and Practical Implications

The introduction of motion-guided latent diffusion presents an exciting frontier for VSR research. The MGLD approach harnesses the generative priors of diffusion models, which have been predominantly explored in single-frame contexts. Through motion guidance and sequential training, the method extends the capabilities of these models into temporally dependent sequences, tackling the notorious flickering artifacts and temporal-blur common in VSR outputs.

Practically, this work has profound implications for enhancing media quality in video streaming, mobile consumption, and emerging mixed-reality applications, where high-quality predictions of video data under bandwidth constraints are critical. The presented method stands as a promising candidate for deployment in scenarios demanding real-time or near-real-time processing, given its efficiency and improved artifact management.

Future Directions

The paper opens avenues for further exploration in optimizing diffusion processes for video tasks, particularly the integration of more advanced flow estimation techniques or alternative temporal modeling strategies. Additionally, scaling this framework to accommodate higher resolutions and frame rates remains a pertinent challenge, as does the pursuit of more efficient computational solutions to balance model capabilities with practical demands.

In conclusion, Motion-Guided Latent Diffusion for VSR offers a sophisticated blend of theoretical novelty and practical effectiveness, marking a significant advancement in addressing the intrinsic complexities of real-world video super-resolution.