Hist2Style: 基于双边网格的直方图引导风格化
Hist2Style: Histogram-Guided Stylization with Bilateral Grids
AI总结：
提出Hist2Style，利用双边网格实现快速、边缘感知的逼真风格迁移，通过蒸馏大模型为轻量网络，并基于直方图嵌入提供可解释的用户控制。
摘要：
逼真风格迁移旨在匹配输入图像与风格目标的颜色和色调，同时保留原始场景的内容和细节。尽管现有的大图像模型可以促进这类外观编辑，但它们的高计算需求、潜在的幻觉以及有限的用户控制使其不适合高分辨率、实时工作流。我们引入Hist2Style，一种双边网格公式，用于快速、边缘感知的风格化，通过将操作限制在双边空间中的局部仿射变换来保持视觉保真度。我们的模型通过在大规模监督语料库上训练（该语料库由语言和视觉语言模型生成），将大图像编辑模型蒸馏为轻量网络，针对空间变化的颜色编辑。网络以风格目标的直方图嵌入为条件，提供可解释的接口，通过修改目标颜色分布来调整输出风格。总体而言，Hist2Style通过构造保持内容结构，避免幻觉，并支持实时、高分辨率的逼真风格化，具有交互式用户可控的颜色和色调调整。
英文摘要：
Photorealistic style transfer aims to match the color and tone of an input image to that of a style target while preserving the content and details of the original scene. Although existing large image models can facilitate these kinds of appearance edits, their high computational demands, potential for hallucinations, and limited user control make them unsuitable for high-resolution, real-time workflows. We introduce Hist2Style, a bilateral-grid formulation for fast, edge-aware stylization that preserves visual fidelity by constraining operations to locally affine transforms in bilateral space. Our model distills a large image editing model into a lightweight network by training on a large supervised corpus generated with language and vision-language models, targeting spatially varying color edits. The network conditions on a histogram-based embedding of the style target to provide an interpretable interface for adjusting the output style by modifying the target color distribution. Overall, Hist2Style maintains content structure by construction, avoids hallucinations, and supports real-time, high-resolution photorealistic stylization with interactive user-controllable color and tone adjustments.
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。
