A contrastive unified encoding framework for sticker style editing

Abstract

Stickers are widely used in digital communication to enhance emotional and visual expressions. The conventional process of creating new sticker pack images involves time-consuming manual drawing, including meticulous color coordination and shading techniques for visual harmony. Learning the visual styles of distinct sticker packs would be critical to the overall process; however, existing solutions usually learn this style information within a limited number of style ''domains'', or per image. In this paper, we propose a contrastive learning framework that allows the style editing of an arbitrary sticker based on one or a number of style references with a continuous manifold to encapsulate all styles across sticker packs. The key to our approach is the encoding of styles into a unified latent space so that each sticker pack correlates with a unique style latent encoding. The contrastive loss ensures identical style latents within the same sticker pack, while distinct styles diverge. Through exposure to diverse sticker sets during training, our model crafts a consolidated continuous latent style space with strong expressive power, fostering seamless style transfer, interpolation, and mixing across sticker sets. Experiments show compelling style transfer results, with both qualitative and quantitative evaluations confirming the superiority of our method over existing approaches.