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    科学领域:

    • 计算机视觉 计算机视觉
    • 三维重建的3D重建
    • 机器学习 机器学习

    背景情况:

    • 从单个图像中生成可控制的3D人类模型是具有挑战性的.
    • 现有的方法经常产生静态模型,缺乏姿势适应性.
    • 对动画和虚拟现实等应用程序来说,控制人类模型姿势至关重要.

    研究的目的:

    • 从单个RGB图像开发一种可控制姿势的人类重建方法.
    • 学习一种动态隐含场,能够在多种姿势中代表人类.
    • 为了克服静态重建和形状姿势纠的局限性.

    主要方法:

    • 构建了一个特征嵌入式人类模型 (FEHM),用于跨姿势的特征传播.
    • 编码了三个姿势脱的特征:全球图像,空间颜色和空间几何.
    • 设计了新的隐性函数来预测动态的人类隐性场.
    • 为了监督,利用了一个大规模的真实的人类化身数据集 (SimuSCAN).

    主要成果:

    • 在3D人体重建方面取得了最先进的性能.
    • 证明有效的姿势控制和形状保存.
    • 成功地将人形与姿势信息分开.

    结论:

    • 拟议的方法可以从单个图像中实现高保真,姿势可控的人体重建.
    • FEHM和姿势脱功能对于动态的人类建模是有效的.
    • 这项工作推进了单图像3D人类重建领域.