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

Diffusion models have exhibited promising progress in video generation.However, they often struggle to retain consistent details within local regionsacross frames. One underlying cause is that traditional diffusion modelsapproximate Gaussian noise distribution by utilizing predictive noise, withoutfully accounting for the impact of inherent information within the inputitself. Additionally, these models emphasize the distinction betweenpredictions and references, neglecting information intrinsic to the videos. Toaddress this limitation, inspired by the self-attention mechanism, we propose anovel text-to-video (T2V) generation network structure based on diffusionmodels, dubbed Additional Perturbation for Latent noise with Adversarialtraining (APLA). Our approach only necessitates a single video as input andbuilds upon pre-trained stable diffusion networks. Notably, we introduce anadditional compact network, known as the Video Generation Transformer (VGT).This auxiliary component is designed to extract perturbations from the inherentinformation contained within the input, thereby refining inconsistent pixelsduring temporal predictions. We leverage a hybrid architecture of transformersand convolutions to compensate for temporal intricacies, enhancing consistencybetween different frames within the video. Experiments demonstrate a noticeableimprovement in the consistency of the generated videos both qualitatively andquantitatively.

APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency