论文标题
实例归一化对基于草图的面部图像生成的细粒度控制的影响
Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation
论文作者
论文摘要
素描是内容创建的直观有效的方法。尽管使用生成的对抗网络已经取得了显着的进展，但对合成含量进行细粒度的控制仍然很具有挑战性。现有图像翻译网络中广泛采用的实例标准化层在输入草图中清除了细节，并导致对生成的面部图像所需形状的精确控制丧失。在本文中，我们全面研究了实例归一化对从手绘草图产生逼真的面部图像的效果。我们首先引入了一种可视化方法，以分析具有一组特定更改的草图的特征嵌入。基于视觉分析，我们修改了基线图像翻译模型中的实例归一层层。我们详细阐述了一套新的手绘草图，其中有11种特殊设计的更改并进行了广泛的实验分析。结果和用户研究表明，我们的方法显着提高了合成图像的质量以及与用户意图的一致性。
Sketching is an intuitive and effective way for content creation. While significant progress has been made for photorealistic image generation by using generative adversarial networks, it remains challenging to take a fine-grained control on synthetic content. The instance normalization layer, which is widely adopted in existing image translation networks, washes away details in the input sketch and leads to loss of precise control on the desired shape of the generated face images. In this paper, we comprehensively investigate the effect of instance normalization on generating photorealistic face images from hand-drawn sketches. We first introduce a visualization approach to analyze the feature embedding for sketches with a group of specific changes. Based on the visual analysis, we modify the instance normalization layers in the baseline image translation model. We elaborate a new set of hand-drawn sketches with 11 categories of specially designed changes and conduct extensive experimental analysis. The results and user studies demonstrate that our method markedly improve the quality of synthesized images and the conformance with user intention.
