本篇論文提出一個基於深度學習，用於人體姿勢估測的方法。我們採用生成對抗網
路作為我們學習模式，其中我們建立了兩個堆疊式沙漏型網路，一個作為生成網
路，一個作為鑑別網路。訓練完成後，生成網路會直接當作人體姿勢估測使用。鑑
別網路用來區分標準答案的熱圖和生成的熱圖，以此計算出的對抗損失反向回饋至
生成網路。此過程能使生成網路學習人體姿勢合理性，且從實驗結果發現，這樣的
訓練有助於提升預測的準確度。

This thesis presents a deep learning based approach to the problem of human pose
estimation. We employ generative adversarial networks as our learning paradigm in
which we set up two stacked hourglass networks with the same architectures, one as
the generator and the other as the discriminator. The generator is used as a human
pose estimator after the training is done. The discriminator distinguishes groundtruth
heatmaps from generated ones, and back-propagates the adversarial loss to the
generator. This process enables the generator to learn the plausible human body
congurations and is shown to be useful for improving the prediction accuracy.

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