TempDiff: Enhancing Temporal-awareness in Latent Diffusion for Real-World Video Super-Resolution

Abstract
Latent diffusion models (LDMs) have demonstrated remarkable success in generative modeling. It is promising to leverage the potential of diffusion priors to enhance performance in image and video tasks. However, applying LDMs to video superresolution (VSR) presents significant challenges due to the high demands for realistic details and temporal consistency in generated videos, exacerbated by the inherent stochasticity in the diffusion process. In this work, we propose a novel diffusionbased framework, Temporal-awareness Latent Diffusion Model (TempDiff), specifically designed for real-world video superresolution, where degradations are diverse and complex. TempDiff harnesses the powerful generative prior of a pre-trained diffusion model and enhances temporal awareness through the following mechanisms: 1) Incorporating temporal layers into the denoising U-Net and VAE-Decoder, and fine-tuning these added modules to maintain temporal coherency; 2) Estimating optical flow guidance using a pre-trained flow net for latent optimization and propagation across video sequences, ensuring overall stability in the generated high-quality video. Extensive experiments demonstrate that TempDiff achieves compelling results, outperforming state-of-the-art methods on both synthetic and real-world VSR benchmark datasets. Code will be available at https://github.com/jiangqin567/TempDiff
Description

        
@article{
10.1111:cgf.15211
, journal = {Computer Graphics Forum}, title = {{
TempDiff: Enhancing Temporal-awareness in Latent Diffusion for Real-World Video Super-Resolution
}}, author = {
Jiang, Qin
and
Wang, Qing Lin
and
Liu, Jie
and
Chi, Li Hua
and
Chen, Xin Hai
and
Zhang, Qing Yang
and
Zhou, Richard
and
Deng, Zheng Qiu
and
Deng, Jin Sheng
and
Tang, Bin Bing
and
Lv, Shao He
}, year = {
2024
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.15211
} }
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