Real-Time Generation Method of Oil Painting Style Brushstrokes Based on Inverse Reinforcement Learning

Abstract

A popular style in modern graphics programs like GIMP, Photoshop, and Painter is brushstroke artwork, a classic artwork that is still extensively practiced today. Regarding successive decision-making situations with ambiguity, reinforcement learning approaches can be quite helpful. In RL, a reward-enabled agent interacts with a dynamic situation to discover a strategy. To use current RL techniques, we must first offer a reward function, a concise depiction of the designer’s purpose. Hence, inverse-RL (IRL), an expansion of RL, was born. It solves this difficulty via developing the reward function from skilled demonstrations. In this article, we present a novel sundry-fidelity Bayesian optimization (SFBO) approach to boost the ability of the IRL regarding oil painting style brushstrokes. Finally, the performance of the proposed approach is examined and compared with the standard approaches to achieve the highest effectiveness in oil painting. The findings are depicted in graphical representation through Origin tool. Approaches based on RL can be quite helpful in ambiguous decision-making situations. Today’s RL approaches must include a reward function, which embodies the designer’s intent. To reduce the dimensions of the data, the proposed SFBO comprises stages of data preprocessing and feature extraction. The proposed technique was evaluated against the existing techniques in terms of accuracy, information loss, average MSE, and time consumption. Compared to the existing approaches, the proposed approach was the most effective.