由参数模型和正常地图指导的隐式3D人类重建
Yong Ren1,2, Mingquan Zhou1,2, Yifan Wang1,2
1School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Journal of imaging
|June 26, 2024
概括
本研究介绍了IHRPN,这是一种从单个图像中创建详细的3D人体模型的新方法,显著改善了松散服装的重建. 该方法提高了3D人体建模的准确性和稳定性,克服了先前技术的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 3D重建的3D重建
- 人类姿势估计 人类姿势估计
背景情况:
- 从单个图像准确的3D人体建模具有挑战性,现有的方法在细节和松散的衣服方面扎.
- 参数化的模型限制了重建,但限制了复杂的服装动态的恢复.
研究的目的:
- 提出一个端到端的方法,IHRPN,从单个2D图像进行强大的3D穿着人类重建.
- 为了提高精度和细节恢复,特别是宽松的衣服,在3D人体模型中.
主要方法:
- 开发了一种IHRPN方法,其中包括一个语义特征提取模块,用于像素到模型空间的一致性.
- 集成的图像功能与SMPL-X网格恢复和正常地图指导隐式表面重建.
- 采用局部特征来实现隐性表面回归,偏离传统的全球方法.
主要成果:
- 在CAPE和AGORA数据集上,IHRPN表现出色.
- 与现有方法相比,实现了明显更准确和更强大的宽松服装重建.
- 验证了语义特征提取和局部隐性表面回归的有效性.
结论:
- 拟议的IHRPN方法显著推进了从单个图像中3D穿着人体重建.
- 实现了卓越的细节和坚固性,特别适用于挑战宽松服装场景.
- 在计算机视觉中提供了一个有前途的方向,以实现更现实的,更准确的人类建模.
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