English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Real-world low-resolution (LR) videos have diverse and complex degradations,imposing great challenges on video super-resolution (VSR) algorithms toreproduce their high-resolution (HR) counterparts with high quality. Recently,the diffusion models have shown compelling performance in generating realisticdetails for image restoration tasks. However, the diffusion process hasrandomness, making it hard to control the contents of restored images. Thisissue becomes more serious when applying diffusion models to VSR tasks becausetemporal consistency is crucial to the perceptual quality of videos. In thispaper, we propose an effective real-world VSR algorithm by leveraging thestrength of pre-trained latent diffusion models. To ensure the contentconsistency among adjacent frames, we exploit the temporal dynamics in LRvideos to guide the diffusion process by optimizing the latent sampling pathwith a motion-guided loss, ensuring that the generated HR video maintains acoherent and continuous visual flow. To further mitigate the discontinuity ofgenerated details, we insert temporal module to the decoder and fine-tune itwith an innovative sequence-oriented loss. The proposed motion-guided latentdiffusion (MGLD) based VSR algorithm achieves significantly better perceptualquality than state-of-the-arts on real-world VSR benchmark datasets, validatingthe effectiveness of the proposed model design and training strategies.

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