论文标题
深层图像空间转换的形象生成
Deep Image Spatial Transformation for Person Image Generation
论文作者
论文摘要
姿势指导的人的形象产生是将源形象转换为目标姿势。此任务需要对源数据的空间操作。但是，卷积神经网络受到空间转换输入的能力的限制。在本文中，我们提出了一个可区分的全球局部局部注意框架，以重新组装功能级别的输入。具体而言，我们的模型首先计算源和目标之间的全局相关性，以预测流场。然后，从特征图中提取流动的局部贴片对，以计算局部注意系数。最后，我们使用内容感知的采样方法与获得的局部注意系数扭曲了源特征。主观和客观实验的结果证明了我们模型的优越性。此外，视频动画和查看合成中的其他结果表明，我们的模型适用于需要空间转换的其他任务。我们的源代码可在https://github.com/renyurui/global-flow-local-cithention上找到。
Pose-guided person image generation is to transform a source person image to a target pose. This task requires spatial manipulations of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this paper, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. Specifically, our model first calculates the global correlations between sources and targets to predict flow fields. Then, the flowed local patch pairs are extracted from the feature maps to calculate the local attention coefficients. Finally, we warp the source features using a content-aware sampling method with the obtained local attention coefficients. The results of both subjective and objective experiments demonstrate the superiority of our model. Besides, additional results in video animation and view synthesis show that our model is applicable to other tasks requiring spatial transformation. Our source code is available at https://github.com/RenYurui/Global-Flow-Local-Attention.
