Research on Animation Character Expression Generation Based on Attention Conditioned Cyclegan
Year: 2024
Type: article
Abstract: With advancements in computer vision and AI, deep learning is applied in animation and game design to enhance facial expression realism. This study proposes ACCGAN, an innovative method for automated animated character expression generation and transfer using deep learning. By integrating cycle consistency, conditional generation, and attention mechanisms, ACCGAN optimizes GANs in capturing complex expression features, maintaining naturalness, and accurately expressing details. ACCGAN utilizes multi-level attention mechanisms, dynamically adjusting focus on key facial features, enhancing capture and expression.
DOI: 10.4018/jcit.356409
Author: Chunling Kong
Institution: Zhengzhou University of Industrial Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403221081