神经-ABC:神经参数模型用于带着衣服的关节身体
IEEE transactions on visualization and computer graphics
|February 12, 2024
概括
神经ABC是一种新型的神经隐性模型,代表着装着的人类,他们的身份,服装,形状和姿势不复杂. 这种参数模型比传统方法更有效地重建形体.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 机器学习 机器学习
背景情况:
- 传统的基于网状网的表现面临着精确模拟各种服装和复杂姿势的形人体的挑战.
- 现有的方法与身体形状,服装风格和姿势变化之间的复杂相互作用作斗争.
研究的目的:
- 介绍Neural-ABC,一种新型参数模型,利用神经隐性函数进行全面的穿着人体表现.
- 制定一个统一的框架来解开和控制身份,服装,形状和姿势参数.
主要方法:
- 使用符号距离函数表示人体,使用无符号距离函数表示服装,统一为无符号距离场.
- 整合可控制的关节结构来构建姿势,并利用神经隐性功能来实现底层表征.
- 在不同数据集的培训中采用脱策略,以确保脱的潜在空间.
主要成果:
- 神经ABC成功地呈现了穿着的人体,并解开了身份,服装,形状和姿势的潜在空间.
- 该模型在从各种输入 (如原始扫描,深度地图和图像) 中重建穿着的人体方面表现出卓越的性能.
- 实现了各种服装类型的准确配合和服装风格的保存,同时适应身体形状和姿势的变化.
结论:
- 神经ABC提供了一种强大而统一的方法来对穿着的人体进行参数建模,克服了传统方法的局限性.
- 模型能够解开属性,允许灵活编辑身份,服装,形状和姿势在生成的人类表示中.
- 神经ABC推进了穿着人体重建和参数建模的最先进技术.
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