Research on Animation Character Expression Generation Based on Attention Conditioned Cyclegan

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.