The proposed model improvesoverthe state-of-the-art methods with better semantic structures and more adaptivestyle patterndetails.

Previousworkseitheruse zeroth-order optimization, which is slow when the set of parameters increases, or rely on a differentiable proxy of the target finishing pipeline, which is hard to train.

This enables us tointerpret which archetypal styles are present inthe input image, and inwhich proportion.

Secondly, TANGO is more robust to the number of vertices (i.e., the mesh quality).

Theproblem is yet challenging and traditionally requires manual efforts from experts ofprofessional artists and a largeamount oftimecost.

Owing to advancements in image synthesis techniques, stylization methodologies for large models have garnered remarkable outcomes. However, when it comes to processing facial images, the outcomes frequently fall short of expectations. Facial stylization is predominantly challenged by two significant hurdles. Firstly, obtaining a large dataset of high-quality stylized images is difficult. The scarcity and diversity of artistic styles make it impractical to compile comprehensive datasets for each style.