“Applying Conditional Information in Guiding Diffusion-Based Method for Anime-Style Face Drawing”, 2024 (; similar):
Anime-style face drawing has become increasingly popular in recent years, with the rise of digital art and animation. However, generating high-quality anime-style faces remains a challenging task, especially when it comes to capturing the details of facial features and expressions.
This thesis explores the application of conditional information in guiding diffusion-based methods for anime-style face drawing. We propose a framework that uses the power of conditional diffusion models to produce accurate and high-quality anime faces. Our approach allows for high control over facial features, expressions, and attributes, enabling the generation of faces that are both esthetically pleasing and semantically meaningful.
We investigate various conditioning mechanisms, including class labels, facial landmarks and sketches, to guide the diffusion process and produce faces that meet specific requirements.
Through experiments, we demonstrate the effectiveness of our method in following the details that user want when generating anime-style faces.
[Keywords: anime-style face drawing, diffusion, conditional information, guiding, digital art, Computer-Aided Design, image generation, face synthesis, machine learning]