论文标题
人姿势和面部图像综合的双方图形推理剂
Bipartite Graph Reasoning GANs for Person Pose and Facial Image Synthesis
论文作者
论文摘要
我们提出了一个新颖的两分图推理生成对抗网络（BIGRAPHGAN），用于两个具有挑战性的任务：人姿势和面部图像合成。所提出的图生成器由两个新的块组成，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，所提出的双方图推理（BGR）块旨在推理源和目标姿势之间的远程交叉关系，这是二分图中的距离，这减轻了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。为了进一步捕获每个部分的姿势的变化，我们提出了一种新颖的部分意识二分图推理（PBGR）块，以分解使用双分裂图推理全局结构转换的任务，以学习不同语义体/面部的不同局部变换。使用三个公共数据集对两个具有挑战性的一代任务进行的实验证明了拟议方法的有效性，以客观的定量分数和主观的视觉现实性。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 Generative Adversarial Network (BiGraphGAN) for two challenging tasks: person pose and facial image synthesis. The proposed graph generator 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 long-range cross relations between the source and target pose in a bipartite graph, which mitigates some of the 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 a person's shape and appearance in an interactive way. To further capture the change in pose of each part more precisely, we propose a novel part-aware bipartite graph reasoning (PBGR) block to decompose the task of reasoning the global structure transformation with a bipartite graph into learning different local transformations for different semantic body/face parts. Experiments on two challenging generation tasks with three public datasets demonstrate the effectiveness of the proposed methods in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN.
