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

Generative models such as StyleGAN2 and Stable Diffusion have achievedstate-of-the-art performance in computer vision tasks such as image synthesis,inpainting, and de-noising. However, current generative models for faceinpainting often fail to preserve fine facial details and the identity of theperson, despite creating aesthetically convincing image structures andtextures. In this work, we propose Person Aware Tuning (PAT) of Mask-AwareTransformer (MAT) for face inpainting, which addresses this issue. Our proposedmethod, PATMAT, effectively preserves identity by incorporating referenceimages of a subject and fine-tuning a MAT architecture trained on faces. Byusing ~40 reference images, PATMAT creates anchor points in MAT's style module,and tunes the model using the fixed anchors to adapt the model to a new faceidentity. Moreover, PATMAT's use of multiple images per anchor during trainingallows the model to use fewer reference images than competing methods. Wedemonstrate that PATMAT outperforms state-of-the-art models in terms of imagequality, the preservation of person-specific details, and the identity of thesubject. Our results suggest that PATMAT can be a promising approach forimproving the quality of personalized face inpainting.

PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting