基于图像条件的二元合成生成算法

生成对抗网络(GAN)是一种无监督学习方法,该算法巧妙地利用博弈的思想来学习生成式模型,但由于GAN通常从单个潜在源采样,因此常常丢失场景中的多个实体交互信息。为了捕获不同对象之间的复杂交互,包括它们的相对缩放,空间布局,遮挡,提出了一种基于图像条件的生成对抗网络,利用"分解—合成"的流程,模型可以根据输入对象的纹理和形状从它们的关节分布生成逼真的合成图像。通过使用Shapenet数据集,在2D和3D图像中分别对55个常见对象类别约51 300个图像模型进行试验,比起传统的SLP和cGAN,算法的图片质量有4%以上的提高。

Binary Synthesis Generation Algorithm Based on Image Conditions

The Generative Adversarial Network (GAN) can generate images with significant complexity and authenticity, but it is usually constructed to sample from a single potential source, thus ignoring spatial interactions between multiple entities that may exist in the scene. To capture the complex interactions between different objects, including their relative scaling, spatial layout, occlusion or view conversion, in this paper we propose an image condition-based generative adversarial network by using decomposition-synthesis procedures. The model can generate realistic composite images from their joint distributions based on their texture and shape of the input object. Through use of Shapenet data set, we experiment on 51 300 image models out of the respective 55 common objects in the 2D and 3D images. Compared with the traditional SLP and cGAN, the image quality in our algorithm can be improved by 4%.