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相关概念视频

Muscles for Facial Expressions01:14

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Gauss's Law: Planar Symmetry01:27

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A planar symmetry of charge density is obtained when charges are uniformly spread over a large flat surface. In planar symmetry, all points in a plane parallel to the plane of charge are identical with respect to the charges. Suppose the plane of the charge distribution is the xy-plane, and the electric field at a space point P with coordinates (x, y, z) is to be determined. Since the charge density is the same at all (x, y) - coordinates in the z = 0 plane, by symmetry, the electric field at P...
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The mean is a measure of the central tendency of a data set. In some data sets, the data is inherently multiplicative, and the arithmetic mean is not useful. For example, the human population multiplies with time, and so does the credit amount of financial investment, as the interest compounds over successive time intervals.
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    此摘要是机器生成的。

    本研究引入了一种使用图形神经网络的新框架,以减少面对面文本生成中的模两可. 该方法有效地从文本描述中创建高质量,属性条件的面部图像.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 基于文本的人脸生成旨在弥合文本和视觉之间的语义差距.
    • 由于文本和面部图像之间的许多对许多映射,现有的方法面临着模两可.
    • 面部部位属性 (几何和外观) 呈现出不符合均分布的相互依赖性.

    研究的目的:

    • 为面对面的文本生成开发一个强大的框架,以减轻模两可.
    • 为了更准确的图像合成,模拟面部部位之间的相互依赖性.
    • 从文本描述中生成高质量的,属性条件化的面部图像.

    主要方法:

    • 引入了一个局部到全球的框架,包含两个图形神经网络 (GNN).
    • 一个GNN模型面部几何学,而另一个模型面部外观.
    • 借助数据集分布学习和部分描述,为改进文本对面映射提供建议.

    主要成果:

    • 拟议的框架有效地模拟了面部部件之间的相互依赖.
    • 从文本输入生成高质量的属性条件面部图像.
    • 与现有的面对面文本生成方法相比,其表现优越的性能和可用性.

    结论:

    • 嵌入式GNN的本地到全球框架成功解决了面对面的文本生成中的模两可.
    • 该方法从数据分布中学习的能力增强了面部图像合成.
    • 这种方法在从文本提示生成真实的面部图像方面取得了重大进展.