Human-AI collaborative artistic creation in oil painting and Chinese ink painting environments using multimodal visual networks

Traditional Chinese ink painting and Western oil painting are two unique artistic media with significant cultural value. Multimodal visual networks are integrated to facilitate real-time interaction between artists and AI systems in creative situations. To combine the smooth, subtle textures of Chinese ink with the bright, textured brushstrokes of oil painting was used. To solve visual network problems, FFA-CausalConvoNet uses several layers and artists’ unique visual components to create works of art that mimic human efforts. The objective is to investigate the human-AI collaborative artistic production by creating a multimodal visual network that combines Chinese ink painting and Western oil painting by utilizing advanced AI techniques. A dataset containing labelled images of Chinese ink paintings and Western oil paintings was utilized for training. Gabor filters were used to capture texture and patterns, separating thin, smooth gradients of ink from rough brushstrokes of oil. Histogram of Oriented Gradients (HOG) and Oriented FAST and Rotated BRIEF (ORB) are used for feature extraction and fusion, which improves texture and shape detection. The proposed generation method utilizes a Flexible Firefly Algorithm Driven Causality-Aware Convolutional Neural Network (FFA-CausalConvoNet) to blend these features and create hybrid artwork that performs well, successfully combining with different styles. The suggested method is implemented by using Python 3.10.1. The resulting artworks displayed a unique blend of the oil painting’s texture and the delicate brushwork of Chinese ink with style fusion accuracy (94.5%), text quality score (9.1 score), processing time (3.5 sec) and cultural fidelity (8.5 score). It highlights the potential of AI-human collaboration in making hybrid art, as the proposed model, FFA-CausalConvoNet, effectively incorporates the distinctive qualities of both art forms, opening up new paths for creative expression in cross-cultural art development.

Authors

Institutions

Publication Details

Journal
Journal of Computational Methods in Sciences and Engineering
Published
2025-06-24
DOI
https://doi.org/10.1177/14727978251352134
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Human-AI collaborative artistic creation in oil painting and Chinese ink painting environments using multimodal visual networks

Jun Cheng, Lili Yang, Liang Li
Journal of Computational Methods in Sciences and Engineering
3D Surveying and Cultural Heritage
article

Human-AI collaborative artistic creation in oil painting and Chinese ink painting environments using multimodal visual networks

Jun Cheng, Lili Yang, Liang Li
article en

Abstract

Traditional Chinese ink painting and Western oil painting are two unique artistic media with significant cultural value. Multimodal visual networks are integrated to facilitate real-time interaction between artists and AI systems in creative situations. To combine the smooth, subtle textures of Chinese ink with the bright, textured brushstrokes of oil painting was used. To solve visual network problems, FFA-CausalConvoNet uses several layers and artists’ unique visual components to create works of art that mimic human efforts. The objective is to investigate the human-AI collaborative artistic production by creating a multimodal visual network that combines Chinese ink painting and Western oil painting by utilizing advanced AI techniques. A dataset containing labelled images of Chinese ink paintings and Western oil paintings was utilized for training. Gabor filters were used to capture texture and patterns, separating thin, smooth gradients of ink from rough brushstrokes of oil. Histogram of Oriented Gradients (HOG) and Oriented FAST and Rotated BRIEF (ORB) are used for feature extraction and fusion, which improves texture and shape detection. The proposed generation method utilizes a Flexible Firefly Algorithm Driven Causality-Aware Convolutional Neural Network (FFA-CausalConvoNet) to blend these features and create hybrid artwork that performs well, successfully combining with different styles. The suggested method is implemented by using Python 3.10.1. The resulting artworks displayed a unique blend of the oil painting’s texture and the delicate brushwork of Chinese ink with style fusion accuracy (94.5%), text quality score (9.1 score), processing time (3.5 sec) and cultural fidelity (8.5 score). It highlights the potential of AI-human collaboration in making hybrid art, as the proposed model, FFA-CausalConvoNet, effectively incorporates the distinctive qualities of both art forms, opening up new paths for creative expression in cross-cultural art development.

Journal of Computational Methods in Sciences and Engineering
Hebei Normal University (CN)
Openalex Percentile: Top 79%
3D Surveying and Cultural Heritage
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.