风格感知的光泽控制用于生成非照片实感渲染
Style-Aware Gloss Control for Generative Non-Photorealistic Rendering
AI总结：
本文提出了一种风格感知的光泽控制方法，通过训练生成模型来解耦光泽与艺术风格，实现对非照片实感图像的精细控制。
摘要：
人类可以通过物体的视觉外观推断材料特性，这种能力也延伸到艺术描绘中，其中相似的感知策略指导着对绘画或素描的解读。在定义材料外观的因素中，光泽与颜色一样，被广泛认为是最重要的因素之一，最近的研究表明，人类可以独立于用于描绘物体的艺术风格来感知光泽。为了研究光泽和艺术风格在学习模型中的表示方式，我们训练了一个无监督生成模型，使用了一个新整理的画家式物体数据集，该数据集旨在系统地变化这些因素。我们的分析揭示了一个层次化的潜在空间，在其中光泽与其他外观因素分离，允许对光泽如何在不同艺术风格中表示和变化进行详细研究。基于这种表示，我们引入了一个轻量级的适配器，将我们的风格和光泽感知的潜在空间连接到潜在扩散模型，使能够合成具有精细控制这些因素的非照片实感图像。我们比较了我们的方法与先前模型，并观察到学习因素的解耦和可控性有所提高。
英文摘要：
Humans can infer material characteristics of objects from their visual appearance, and this ability extends to artistic depictions, where similar perceptual strategies guide the interpretation of paintings or drawings. Among the factors that define material appearance, gloss, along with color, is widely regarded as one of the most important, and recent studies indicate that humans can perceive gloss independently of the artistic style used to depict an object. To investigate how gloss and artistic style are represented in learned models, we train an unsupervised generative model on a newly curated dataset of painterly objects designed to systematically vary such factors. Our analysis reveals a hierarchical latent space in which gloss is disentangled from other appearance factors, allowing for a detailed study of how gloss is represented and varies across artistic styles. Building on this representation, we introduce a lightweight adapter that connects our style- and gloss-aware latent space to a latent-diffusion model, enabling the synthesis of non-photorealistic images with fine-grained control of these factors. We compare our approach with previous models and observe improved disentanglement and controllability of the learned factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
