POCE:可以控制姿势的表情编辑
概括
这项研究介绍了POCE,这是一个用于现实的面部表情编辑的新型网络. 它可以使用未配对图像控制头部姿势,克服现有方法的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像合成 图像合成
背景情况:
- 通过深度神经网络,面部表情编辑正在进步.
- 现有的方法难以处理姿势变化,需要配对数据.
- 这限制了现实的编辑和可用性.
研究的目的:
- 为了介绍POCE,一个可控制姿势的表情编辑网络.
- 为了同时实现现实的面部表情和头部姿势生成.
- 使用未配对的训练图像进行可访问的编辑.
主要方法:
- POCE 地图将图像面向UV空间进行脱的编辑.
- 自主监督的紫外线补充解决了遮和缺失的纹理.
- 微弱监督的紫外线编辑产生了新的表达方式,同时保留了身份.
主要成果:
- POCE有效地从未配对的面部图像中学习.
- 该网络产生现实的,高真实度的面部表情.
- 在各种新的头部姿势下,编辑是可以控制的.
结论:
- POCE为可控制姿势的面部表情编辑提供了一个创新的解决方案.
- 该方法通过解开姿势和表达方式来增强现实主义和可用性.
- 它在未配对的数据中表现出强的性能.
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