PaintFlow: A Unified Framework for Interactive Oil Paintings Editing and Generation
Zhangli Hu, Ye Chen, Jiajun Yao, Bingbing Ni

TL;DR
PaintFlow introduces a comprehensive multimodal framework for interactive oil painting generation and editing, combining reference images, sketches, and natural language prompts to produce stylistically consistent artworks with fine-grained control.
Contribution
The paper presents a novel unified system that integrates multimodal inputs, a self-supervised style transfer pipeline, and advanced feature integration to enhance oil painting creation and editing.
Findings
Enables precise semantic control in oil painting editing.
Creates a large-scale stylized oil painting dataset via self-supervised style transfer.
Achieves high-quality, stylistically consistent oil paintings with interactive editing capabilities.
Abstract
Oil painting, as a high-level medium that blends human abstract thinking with artistic expression, poses substantial challenges for digital generation and editing due to its intricate brushstroke dynamics and stylized characteristics. Existing generation and editing techniques are often constrained by the distribution of training data and primarily focus on modifying real photographs. In this work, we introduce a unified multimodal framework for oil painting generation and editing. The proposed system allows users to incorporate reference images for precise semantic control, hand-drawn sketches for spatial structure alignment, and natural language prompts for high-level semantic guidance, while consistently maintaining a unified painting style across all outputs. Our method achieves interactive oil painting creation through three crucial technical advancements. First, we enhance the…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Aesthetic Perception and Analysis
