We propose a general purpose approach to detect landmarks with improved temporal consistency, and personalization. Most sparse landmark detection methods rely on laborious, manually labelled landmarks, where inconsistency in annotations over a temporal volume leads to sub-optimal landmark learning. Further, high-quality landmarks with personalization is often hard to achieve. We pose landmark detection as an image translation problem. We capture two sets of unpaired marked (with paint) and unmarked videos. We then use a generative adversarial network and cyclic consistency to predict deformations of landmark templates that simulate markers on unmarked images until these images are indistinguishable from ground-truth marked images. Our novel method does not rely on manually labelled priors, is temporally consistent, and image class agnostic -- face, and hand landmarks detection examples are shown.

中文翻译：

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生成地标

我们提出了一种通用的方法来检测具有改进的时间一致性和个性化的地标。大多数稀疏的地标检测方法依赖于费力的，手动标记的地标，其中时间量上的注释不一致会导致次优的地标学习。此外，通常很难实现具有个性化的高质量地标。我们将地标检测作为图像翻译问题。我们捕获了两组未配对的已标记（带油漆）和未标记的视频。然后，我们使用生成的对抗网络和循环一致性来预测界标模板的变形，这些模板将模拟未标记图像上的标记，直到这些图像与地面真实标记图像无法区分为止。我们的新颖方法不依赖人工标记的先验，在时间上是一致的，并且图像类别不可知-面部，