description: <p>NPR (Non-Photorealistic Rendering), also known as expressive or hand-drawn rendering, is a technique that simulates hand-drawn or cartoonish effects in two-dimensional or three-dimensional scenes rather than creating realistic photographic effects like traditional rendering techniques. For example, NPR technology was used in "The Legend of Zelda: Breath of the Wild" to create a dreamlike visual experience. Due to the unique style created by NPR, some artists intentionally pursue and emulate NPR effects in their works, appreciating the non-photorealistic and artistic expression it offers. Therefore, NPR technology is also considered a means of creation and expression, providing artists with more creative space and inspiration.</p><p>However, implementing NPR requires specific rendering algorithms, which often necessitate the creation of custom shaders. Developing shaders requires a certain degree of computer graphics and programming knowledge, which may be daunting for artists who need more relevant backgrounds. Moreover, writing shaders requires a solid mathematical foundation in linear algebra, calculus, and other mathematical concepts, specific hardware and software support, and repeated experimentation and debugging, all of which make shader development quite challenging.</p><p>In light of these challenges, this paper proposes using deep learning to train an NPR-style model as a feasible solution for NPR rendering. Taking the typical three-to-two technique in NPR as an example, we prepare a complete dataset, train the LoRA model, evaluate its performance, and optimize it through various steps to obtain a model that can simulate the three-to-two style effectively. The results show that the model has a good fit and high robustness. This research provides a new perspective for achieving NPR style and may inspire further research.</p><p> </p><p>NPR（Non-Photorealistic Rendering），即非真实感渲染，也被称为表现性渲染或手绘渲染。与传统的渲染技术不同，NPR不试图创造真实的照片效果，而是尝试在二维或三维场景中模拟出手绘或卡通的效果。比如，《塞尔达传说 旷野之息》中的游戏画面就采用了NPR技术，使得游戏画面呈现出一种梦幻般的演出效果。由于NPR所营造的独特画面风格，有些艺术家甚至在其作品中刻意追求、还原NPR所创造出的非真实感和艺术表现力。因此，NPR技术也被认为是一种创作和表现的手段，为艺术家们提供更多的创作空间和灵感。</p><p>然而，实现NPR通常需要特定渲染算法，而这些算法往往需要编写自定义着色器（Shader）来实现。编写着色器需要具备一定的计算机图形学和编程技能，对普遍缺乏相关背景的艺术家们而言，学习成本较高。同时，编写着色器也要求一定的数学基础，如线性代数、微积分等，还需要一定的硬件和软件支持，及反复的实验和调试，这些都使得编写着色器的难度大大提高。</p><p>基于此，本文提出使用深度学习训练NPR风格的模型作为实现NPR渲染的一种可行方案。以NPR中典型的三渲二技术为例，通过完整的数据集准备、模型训练、模型评估及模型优化等步骤，训练出仿三渲二风格的LoRA模型。结果显示，模型拟合程度较好，鲁棒性较高。研究结果可为实现NPR风格提供新的思路。</p><p></p><p></p><p>注：由于Civitai展示页所记录的图像生成参数并不完整，固将模型展示所使用的13张图片（含生成数据）的图像链接单独列于此处：</p><p>链接：<a target="_blank" rel="ugc" href="https://pan.baidu.com/s/1sAAtLDINX_Tatp9WrL1-Cg?pwd=ak97">https://pan.baidu.com/s/1sAAtLDINX_Tatp9WrL1-Cg?pwd=ak97</a></p><p>提取码：ak97</p>
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name: NPR Style Model
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