Abstract
In recent years, the recognition of free-hand sketches has remained a popular task. However, in some special fields such as the military field, free-hand sketches are difficult to sample on a large scale. Common data augmentation and image generation techniques are difficult to produce images with various free-hand sketching styles. Therefore, the recognition and segmentation tasks in related fields are limited. In this paper, we propose a novel adversarial generative network that can accurately generate realistic free-hand sketches with various styles. We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set. We further demonstrate with qualitative and quantitative evaluations our advantages in visual quality, content accuracy, and style imitation on SketchIME.
Abstract (translated)
近年来，自由手绘图的识别仍然是一个流行的任务。然而，在一些特殊领域，如军事领域，手绘图在大规模上很难进行抽样。常见的数据增强和图像生成技术很难生成各种自由手绘图形的图像。因此，相关领域的识别和分割任务存在局限性。在本文中，我们提出了一种新颖的对抗生成网络，可以准确生成各种风格的手绘现实图。我们研究了模型的性能，包括使用从先验正态分布中随机采样各种样式来生成具有各种自由手绘图式风格的图像，将画家的风格与已知的手绘图分离以生成具有特定风格的手绘图，以及生成不在训练集中的未知类别的图像。我们进一步通过质性和量化评估展示了我们在SketchIME在视觉质量、内容准确性和风格模仿方面的优势。
URL
https://arxiv.org/abs/2401.04739
