用直方图控制颜色风格，实时实现高分辨率保真图像风格迁移。
Hist2Style: Histogram-Guided Stylization with Bilateral Grids
- 基于双网格的局部仿射变换，保持边缘清晰
- 支持实时高分辨率处理，延迟低于50ms
- 用户可交互调整颜色分布，效果直观可控
真实感风格迁移旨在将输入图像的颜色与色调匹配到风格目标，同时保留原始场景的内容和细节。尽管现有大型图像模型可支持此类外观编辑，但其高计算开销、潜在幻觉及有限的用户控制使其不适合高分辨率、实时工作流。我们提出Hist2Style，一种基于双网格的快速、边缘感知的风格化方法，通过在双网格空间中限制为局部仿射变换来保持视觉保真度。该模型通过在由语言和视觉-语言模型生成的大规模监督语料上训练，将大型图像编辑模型压缩为轻量级网络，专注于空间变化的颜色编辑。网络以风格目标的直方图嵌入作为条件，提供可解释的接口，用户可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、高分辨率的真实感风格迁移，具备交互式颜色与色调调节能力。
原文摘要 · Abstract (English)
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.
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