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相关概念视频

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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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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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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面对细粒度的说话,面对一代人的面孔.

Zhicheng Sheng, Liqiang Nie, Meng Liu

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    此摘要是机器生成的。

    我们介绍了Fine-gRained mOtioN 模型 (FROND),用于合成高保真性说话面孔视频. FROND通过将运动轨迹精制成一个连续的密集运动场来产生生动的面部表情和平滑的过渡.

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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:

    • 说话面孔的生成合成了从肖像和音频的唇同步视频.
    • 现有的方法在生动的面部表情,平滑的过渡和肖像细节的保存方面扎.
    • 以前的方法往往会导致低保真度的视频与不自然的面部肌肉运动.

    研究的目的:

    • 提出一种新型模型,Fine-gRained mOtioN moDel (FROND),用于高保真度的说话面孔生成.
    • 为了解决捕捉面部表情,确保时光光滑和保持肖像细节的局限性.
    • 为了提高合成的说话面孔视频的现实性和质量.

    主要方法:

    • 一个双流编码器捕获了局部面部关键点运动和全球运动背景.
    • 一个运动估计模块预测音频驱动的运动,学习连续轨迹平滑的时间和空间运动.
    • 一个基于隐式神经网络的解码器可以恢复高频细节,用于高保真度的视频合成.

    主要成果:

    • FROND将面部关键点轨迹改进为一个连续的密集运动场.
    • 该模型有效地融合了本地和全球运动信息,以实现流的运动.
    • 定量和定性评价表明性能优于最先进的基线.

    结论:

    • FROND显著提高了说话面孔生成的质量.
    • 提出的方法在合成现实和详细的谈话面孔视频方面取得了最先进的结果.
    • FROND提供了一个强大的解决方案,用于生成细粒度的面部动画.