SMPLer:为单眼3D人类形状和姿势估计服变压器
IEEE transactions on pattern analysis and machine intelligence
|December 13, 2023
概括
新的Transformer框架SMPLer通过高分辨率特征的高效使用来增强单眼的3D人形和姿势估计. 这种方法可以用更少的参数实现最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人的姿势估计 人的姿势估计
背景情况:
- 现有的用于3D人类估计的变压器模型面临着具有高分辨率功能的计算挑战.
- 方位复杂性限制了精细细节的使用,这对于准确的重建至关重要.
研究的目的:
- 开发一个超越单眼3D人形和姿势估计计算局限性的变压器框架.
- 为了实现高分辨率特征的有效利用,以提高重建准确度.
主要方法:
- 建议SMPLer,一个转换器框架,包含一个脱的注意力操作.
- 引入了基于SMPL的目标表示,以有效地利用高分辨率的功能.
- 开发了新的多尺度和共同意识的注意模块.
主要成果:
- 与现有方法相比,SMPLer表现出优越的定量和质量性能.
- 在人类3.6M数据集上获得了45.2mm的平均每关节位置误差 (MPJPE).
- 在不到三分之一的参数下,以超过10%的速度超过了最先进的技术.
结论:
- SMPLer有效地解决了变压器模型在3D人类估计中的计算约束.
- 拟议的框架显著提高了单眼3D人形和姿势重建的准确性和效率.
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