论文标题
二分图推理剂量的人图像产生
Bipartite Graph Reasoning GANs for Person Image Generation
论文作者
论文摘要
我们为具有挑战性的人形象生成任务提供了一种新颖的二分图推理gan（Bigraphgan）。提出的图生成器主要由两个新的块组成，旨在分别对姿势到姿势和姿势形象关系进行建模。具体而言，所提出的两分图推理（BGR）块旨在推理源姿势与目标姿势之间的远距离关系，这是在两部分图中的，这减轻了由姿势变形引起的一些挑战。此外，我们提出了一个新的相互作用和聚集（IA）块，以有效地更新和增强人的形状和外观的特征表示能力，以交互式方式。在两个具有挑战性和公共数据集（即Market-1501和DeepFashion）上进行的实验表明了拟议的Bigraphgan在客观的定量分数和主观的视觉现实方面的有效性。源代码和训练有素的模型可在https://github.com/ha0tang/bigraphgan上找到。
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN.
