OmniStyle-INR：用于隐式神经表示的通用多模态风格迁移

中文摘要 AI 辅助

风格迁移在各种数据模态中都是一项基础且重要的任务，能实现基于参考图像和文本描述的创意操作。近期利用高斯喷绘的方法成为二维图像、视频、三维场景和四维动态的统一表示，但用于视频和二维图像在结构上并非最优。相比之下，隐式神经表示在所有这些数据领域更受欢迎。为此，我们引入OmniStyle-INR，一个利用基于网络的连续表示作为通用域的新框架，能在文本提示和视觉示例引导下跨所有视觉模态成功进行高质量风格迁移。

英文摘要

Style transfer remains a fundamental and highly important task across various data modalities, enabling creative manipulation conditioned by both reference images and textual descriptions. Recently, methods utilizing Gaussian Splatting have emerged as a unified representation for 2D images, video, 3D scenes, and 4D dynamics. However, representing videos and 2D images with Gaussian Splatting is structurally sub-optimal for dense continuous domains. The number of required Gaussians often approaches the total number of pixels, raising questions about the actual utility of such a representation for these specific modalities. In contrast, Implicit Neural Representations have established themselves as a much more popular and natural choice across all these data domains. Implicit Neural Representations naturally provide significant advantages, including data compression, inherent capabilities for super resolution, and seamless integration with deep generative models. To this end, we introduce OmniStyle-INR, a novel framework that leverages network-based continuous representations as a truly universal domain. Our approach successfully performs high-quality style transfer across all visual modalities, guided seamlessly by both text prompts and visual exemplars.

发表机构

• Jagiellonian University（雅盖隆大学）
• IDEAS Research Institute（IDEAS 研究所）

机构由 AI 辅助整理，请以论文原文为准。