When adding a photo onto a graphic design, professional graphic designers often adjust its colors based on some target colors obtained from the brand or product to make the entire design more memorable to audiences and establish a consistent brand identity. However, adjusting the colors of a photo in the context of a graphic design is a difficult task, with two major challenges: (1) Locality: The color is often adjusted locally to preserve the semantics and atmosphere of the original image; and (2) Naturalness: The modified region needs to be carefully chosen and recolored to obtain a semantically valid and visually natural result. To address these challenges, we propose a learning-based approach to photo color adjustment for graphic designs, which maps an input photo along with the target colors to a recolored result. Our method decomposes the color adjustment process into two successive stages: modifiable region selection and target color propagation. The first stage aims to solve the core, challenging problem of which local image region(s) should be adjusted, which requires not only a common sense of colors appearing in our visual world but also understanding of subtle visual design heuristics. To this end, we capitalize on both natural photos and graphic designs to train a region selection network, which detects the most likely regions to be adjusted to the target colors. The second stage trains a recoloring network to naturally propagate the target colors in the detected regions. Through extensive experiments and a user study, we demonstrate the effectiveness of our selective region-based photo recoloring framework.

中文翻译：

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用于图形设计的基于区域的选择性照片颜色调整

在将照片添加到平面设计中时，专业的平面设计师通常会根据从品牌或产品中获得的一些目标颜色来调整其颜色，以使整个设计更容易被观众记住，并建立一致的品牌标识。然而，在平面设计的背景下调整照片的颜色是一项艰巨的任务，面临两大挑战： (1) 局部性：通常在局部调整颜色以保留原始图像的语义和氛围；(2) 自然性：修改区域需要仔细选择和重新着色，以获得语义上有效且视觉上自然的结果。为了应对这些挑战，我们提出了一种基于学习的图形设计照片颜色调整方法，该方法将输入照片与目标颜色一起映射到重新着色的结果。我们的方法将颜色调整过程分解为两个连续的阶段：可修改区域选择和目标颜色传播。第一阶段旨在解决应调整哪些局部图像区域的核心且具有挑战性的问题，这不仅需要对我们视觉世界中出现的颜色有常识，还需要了解微妙的视觉设计启发式方法。为此，我们利用自然照片和图形设计来训练区域选择网络，该网络检测最有可能调整为目标颜色的区域。第二阶段训练重新着色网络在检测到的区域中自然传播目标颜色。通过广泛的实验和用户研究，我们证明了我们基于选择性区域的照片重新着色框架的有效性。