神经小说演员:学习人类演员的通用化可动画的神经表征
IEEE transactions on visualization and computer graphics
|August 16, 2023
概括
这项研究引入了一种创新的方法,用于从图像中创建可动画的人类模型. 新的方法可以产生新的视图,并激活任何有姿势控制的人,超越现有技术.
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:
- 从有限的数据中合成新的人类观点和动画是具有挑战性的.
- 现有的方法往往难以将其推广到新的个体或提供用户控制的动画.
- 需要一种统一的方法,既解决了新的视图合成,也为多人提供可控制的动画.
研究的目的:
- 开发一个通用的可动画的神经人类表示.
- 从稀疏的多视图图像中为任意人群实现新视图和用户控制动画的合成.
- 克服现有方法的局限性,即无法同时实现概括和可控制动画.
主要方法:
- 学习使用稀疏的多视图图像集来进行通用的可动画的神经人类表示.
- 在共享多人的人类模型中使用3D代理.
- 扭曲姿势空间到共享的规范姿势空间用于神经场学习.
- 设计一个神经人类模型,解开几何和外观,以处理形状,姿势和服装的变化.
- 利用3D代理的空间和表面点的图像特征.
主要成果:
- 提出的方法成功地合成了任意人的新视图图像.
- 学习的表示允许动画个体与用户的姿势控制.
- 与最先进的方法相比,该方法在新视图合成和动画任务中表现出更高的性能.
- 解的几何和外观有助于处理复杂的人类变异.
结论:
- 开发的方法在创建通用可动画的神经人类表征方面取得了重大进展.
- 这种方法有效地结合了新的视图合成和用户控制的多人动画功能.
- 该技术为复杂的人类建模挑战提供了强大的解决方案,优于现有方法.
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