Improving Key Human Features for Pose Transfer

Victor-Andrei Ivan, Ionut Mistreanu, Andrei Leica, Sung-Jun Yoon, Manri Cheon, Junwoo Lee, Jinsoo Oh
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引用次数: 2

Abstract

It is still a great challenge in the Pose Transfer task to generate visually coherent images, to preserve the texture of clothes, to maintain the source identity and to realistically generate key human features such as the face or the hands. To tackle these challenges, we first conduct a study to obtain the most robust conditioning labels for this task and the baseline method [44] that we choose. We then improve upon the baseline by including deep source features from an Auto-encoder through an Attention mechanism. Finally we add region discriminators that are focused on key human features, thus obtaining results competitive with the state-of-the-art.
改进姿势转移的关键人体特征
如何生成视觉上连贯的图像,保持服装的纹理,保持源身份,逼真地生成人脸或手等关键人体特征,仍然是Pose Transfer任务中的一个巨大挑战。为了应对这些挑战,我们首先进行了一项研究,以获得该任务最稳健的条件反射标签和我们选择的基线方法[44]。然后,我们通过注意机制包括来自自编码器的深层源功能,从而在基线上进行改进。最后,我们添加了专注于关键人类特征的区域鉴别器,从而获得了与最先进的结果相竞争的结果。
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