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相关概念视频

Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Parametric Surfaces01:30

Parametric Surfaces

A parametric surface in three-dimensional space is defined through a vector-valued function\begin{equation*}\mathbf{r}(u, v) = x(u, v)\mathbf{i} + y(u, v)\mathbf{j} + z(u, v)\mathbf{k}\end{equation*}where u and v are parameters within a specified domain D in the uv-plane. The functions x(u, v), y(u, v), and z(u, v) define the coordinates of points on the surface. As u and v vary over D, the position vector r(u, v) traces a continuous surface in space. This parametric representation is essential...

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相关实验视频

Updated: Jun 8, 2026

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression
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The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal Skin and Melanoma Progression

Published on: August 3, 2011

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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
PubMed
概括
此摘要是机器生成的。

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.

关键词:
人类重建的人类重建地方特征 地方特征 地方特征参数模型是一个参数模型.

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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.