InstaHMR:实例意识的单阶段多人的人类网络恢复
IEEE transactions on visualization and computer graphics
|April 19, 2024
概括
这项研究介绍了InstaHMR,这是一个用于从图像中准确的3D人体网格恢复的新型网络. 它通过使用上下文实例指导 (CIG) 和辅助损失来增强单人特征,在多个数据集上实现最先进的结果.
科学领域:
- 计算机视觉和机器学习
- 3D人体姿势和形状估计 3D人体姿势和形状估计
背景情况:
- 从图像中估计3D人体网格对于各种应用至关重要.
- 现有的单阶段方法往往难以准确的网格表示,特别是在多人场景中.
研究的目的:
- 提出一个实例意识的多人 3D 人类网格恢复 (InstaHMR) 网络.
- 通过利用实例意识功能来提高3D人体网格恢复的准确性.
主要方法:
- 开发了一个包含实例意识方法的单阶段框架.
- 引入了上下文实例指导 (CIG) 模块,使用实例特定特征的空间和通道注意力.
- 实现了两个辅助损失:人类三重平面 (HTP) 损失用于关节位置准确性和T-pose Shape (TS) 损失用于抽象形状学习.
主要成果:
- 拟议的CIG模块有效地保留特定实例信息,优于像素级特征.
- 辅助损失有助于捕捉微妙的关节差异和学习形状参数.
- 在四个基准多人数据集上实现了最先进的性能.
结论:
- 在多人 3D 人类网格恢复中,InstaHMR 展示了卓越的性能.
- 实例意识的特征表示和新的损失函数是模型成功的关键.
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