无需训练即可高保真还原设计稿细节与材质。
LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model
- 通过分频线融合与分层着色，模拟人类绘画认知过程
- 在真实感、结构准确性和材质精度上超越现有方法
- 适合设计师快速生成高质量效果图，无需建模或调参
从线稿生成图像在设计与图像生成中至关重要，能降低制作成本，但专业线稿需保留复杂细节。文本提示难以保证准确性，图像转换则存在一致性差与细粒度控制难的问题。我们提出LineArt，一种无需训练的框架，可将复杂外观精准迁移至精细设计线稿，助力设计与艺术创作。该方法通过模拟分层视觉认知并融合人类艺术经验，引导扩散过程，在保持结构准确性的同时生成高保真外观。其无需精确3D建模、物理参数或网络训练，极大提升设计效率。系统包含两阶段：多频线融合模块补充结构细节，以及基础层塑造与表面层着色的双阶段绘画流程。我们还构建了新数据集ProLines用于评估。实验表明，LineArt在准确性、真实感和材质精度上均优于当前最优方法。
原文摘要 · Abstract (English)
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.
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