Data-Driven NPR Illustrations of Natural Flows in Chinese Painting

Introducing motion into existing static paintings is becoming a field that is gaining momentum. This effort facilitates keeping artworks current and translating them to different forms for diverse audiences. Chinese ink paintings and Japanese Sumies are well recognized in Western cultures, yet not easily practiced due to the years of training required. We are motivated to develop an interactive system for artists, non-artists, Asians, and non-Asians to enjoy the unique style of Chinese paintings. In this paper, our focus is on replacing static water flow scenes with animations. We include flow patterns, surface ripples, and water wakes which are challenging not only artistically but also algorithmically. We develop a data-driven system that procedurally computes a flow field based on stroke properties extracted from the painting, and animate water flows artistically and stylishly. Technically, our system first extracts water-flow-portraying strokes using their locations, oscillation frequencies, brush patterns, and ink densities. We construct an initial flow pattern by analyzing stroke structures, ink dispersion densities, and placement densities. We cluster extracted strokes as stroke pattern groups to further convey the spirit of the original painting. Then, the system automatically computes a flow field according to the initial flow patterns, water boundaries, and flow obstacles. Finally, our system dynamically generates and animates extracted stroke pattern groups with the constructed field for controllable smoothness and temporal coherence. The users can interactively place the extracted stroke patterns through our adapted Poisson-based composition onto other paintings for water flow animation. In conclusion, our system can visually transform a static Chinese painting to an interactive walk-through with seamless and vivid stroke-based flow animations in its original dynamic spirits without flickering artifacts.