AI Art Generation

• Key Focus Areas:

• Human-Like Painting Logic Modeling: Leveraging diffusion models (e.g., Stable Diffusion) and generative adversarial networks (GANs) to simulate human-like stroke-by-stroke painting logic and creative decision-making processes.

• Artistic Style Transfer and Control: Developing multimodal generative models (e.g., Stroke-GAN Painter) for style-guided artwork generation and controllable editing from text or sketches.

• Quality Evaluation of AI Art: Establishing theoretical frameworks and quantitative metrics (e.g., aesthetic consistency, creativity) to assess AI-generated artworks.

• Applications: Digital art creation, animation design, cultural heritage restoration.

Research Features

• Interdisciplinarity: Combines computer science, art, and pedagogy, emphasizing both technical rigor and humanistic values.

• Cutting-Edge Technologies: Supported by National Natural Science Foundation projects, focusing on diffusion models, lightweight networks, and real-world problem-solving.

• Industry-Academia Collaboration: Partners with companies like Zhuhai Vision Technology to industrialize AI algorithms in smart healthcare and industrial design.

Join the Research Group to Engage in:

• National-level projects exploring frontier AI models and vision algorithms;

• Cross-border collaborations with institutions like Hong Kong Polytechnic University;

• End-to-end research from theory to application, with opportunities for high-impact publications or patents.

Together, Let’s Shape the Future of AI-Powered Art and Vision Science!