LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model

TL;DR

LineArt is a training-free diffusion-based framework that accurately transfers high-quality appearance to detailed design drawings, overcoming style degradation and fine-grained control issues without requiring 3D models or network training.

Contribution

It introduces a novel, training-free approach combining hierarchical visual cognition and artistic experience for appearance transfer in design drawings.

Findings

Outperforms state-of-the-art methods in accuracy and realism

Requires no 3D modeling or network training

Effective in preserving structural details and style

Abstract

Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image translation struggles with consistency and fine-grained control. We present LineArt, a framework that transfers complex appearance onto detailed design drawings, facilitating design and artistic creation. It generates high-fidelity appearance while preserving structural accuracy by simulating hierarchical visual cognition and integrating human artistic experience to guide the diffusion process. LineArt overcomes the limitations of current methods in terms of difficulty in fine-grained control and style degradation in design drawings. It requires no precise 3D modeling, physical property specs, or network training, making it more convenient for design tasks. LineArt consists of two stages: a multi-frequency lines fusion module to supplement the input design drawing with detailed structural information and a two-part painting process for Base Layer Shaping and Surface Layer Coloring. We also present a new design drawing dataset ProLines for evaluation. The experiments show that LineArt performs better in accuracy, realism, and material precision compared to SOTAs.