Interactive Drawing Interface for Aging Anime Face Sketches Using Transformer-Based Generative Model

Abstract

Drawing anime characters with facial features of different ages is a challenging task. The characters’ facial features vary significantly with age, making it especially difficult for beginners to depict age-specific anime characters accurately. In this paper, we propose AgeFace, an interactive drawing interface designed to help users draw high-quality anime faces for characters of multiple age groups. AgeFace can provide a combination of local and global user guidance in the drawing process to enhance both detailed facial features and the overall aging features. Local guidance assists users in drawing detailed facial features, while global guidance provides hints for the overall layout of the face and additional features, such as wrinkles. During the local guidance stage, we apply an image retrieval approach to provide detailed instructions on facial features. In the global guidance stage, we propose the Transformer-based sequential generation model to create entire anime faces from drawn stroke sequences. The proposed framework of AgeFace combines a data-driven retrieval method and the generation model to provide users with inspiration during the drawing process. To verify the effectiveness of our guidance, we conducted user studies and comparison experiments with existing sketch generation models. The results demonstrated that AgeFace can significantly help users create multi-age anime faces and validate the effectiveness of our proposed generative model.

Keywords

• Multiage anime faces
• sketch-based image retrieval
• data-driven drawing support
• sequential generation model