H4MER:通过学习用变压器的神经组合表征来进行人类4D建模
IEEE transactions on pattern analysis and machine intelligence
|September 11, 2023
概括
本研究介绍了Human 4D Modeling with transformER (H4MER),这是一个用于动态3D人类建模的新型神经表示. H4MER有效地捕捉了4D人类数据的详细几何和精确运动.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 3D人体建模 3D人体建模
背景情况:
- 深度学习已经推进了3D重建,但模拟复杂几何结构的动态4D人类捕获仍然未被探索.
- 现有的方法往往难以代表人类运动和形状的全部复杂性.
研究的目的:
- 介绍一种新的神经组成表示,人类4D模型与转换器 (H4MER),用于动态3D人类建模.
- 开发一种能够用详细的几何形状和精确的运动时间来表示动态人体的方法.
主要方法:
- H4MER通过利用SMPL参数模型对人体前置来利用一个紧的组成表示.
- 一个线性运动模型提供了初始姿势估计,辅助代码处理每姿势和几何细化.
- 基于变压器的特征提取器和条件GRU解码器用于增强学习和表示.
主要成果:
- H4MER成功地恢复了动态的人类,精确的运动和详细的几何形状.
- 该方法在各种4D人类相关任务中表现出有效性,包括单眼视频装配和运动重定位.
- 实验证实了H4MER表示的稳定性和多功能性.
结论:
- H4MER为4D人体建模提供了一种强大而灵活的方法,推进了动态人体捕捉领域.
- 拟议的表示适用于计算机视觉和图形领域的一系列下游应用.
- 这项工作为更复杂,更准确的人类动态建模铺平了道路.
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