Painting Style Alignment: Restoration of Ancient Chinese Landscape Paintings Driven by Aesthetic Cognition and Aesthetic Computation

Abstract

Painting style alignment is critical for ancient painting restoration. To reduce the aesthetic cognition conflicts between human and artificial intelligence in Chinese landscape painting restoration, we propose a new research paradigm, joint aesthetic cognition, which aligns painting style in three stages: descriptive painting style based on human aesthetic cognition, predictive painting style with AI aesthetic computation, and prescriptive restoration test via human-AI collaboration. To keep interaction of these stages continuous, a hybrid research method based on both data design and self-supervised learning is further proposed to adaptively construct the joint cognition of specific painting style. Preliminary tests on two restorative scenarios, analogized generation and cognitive recompositing, indicate that our joint aesthetic cognition is effective and feasible for painting style alignment, and can thus facilitate human-AI collaboration in ancient painting restoration.