论文标题
多尺度注意指导姿势转移
Multi-scale Attention Guided Pose Transfer
论文作者
论文摘要
姿势转移是指以前看不见的小说姿势的人的概率图像产生，从另一个人的姿势不同。由于潜在的学术和商业应用，近年来对此问题进行了广泛的研究。在解决问题的各种方法中，注意力指导的渐进生成在大多数情况下会产生最新的结果。在本文中，我们通过在编码器和解码器的每个分辨率级别引入注意力链接来提出改进的姿势转移网络体系结构。通过利用这种密集的多尺度注意指导方法，我们能够在视觉和分析上对现有方法实现重大改进。我们通过与DeepFashion数据集上的几种现有方法进行了广泛的定性和定量比较来结束我们的发现。
Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, this problem is extensively studied in recent years. Among the various approaches to the problem, attention guided progressive generation is shown to produce state-of-the-art results in most cases. In this paper, we present an improved network architecture for pose transfer by introducing attention links at every resolution level of the encoder and decoder. By utilizing such dense multi-scale attention guided approach, we are able to achieve significant improvement over the existing methods both visually and analytically. We conclude our findings with extensive qualitative and quantitative comparisons against several existing methods on the DeepFashion dataset.
