用流形学习生成更自然的人体动作，提升虚拟角色真实感。
Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold
- 通过流形学习从复杂数据中提取人体动作的低维有效子空间
- 相比传统方法，生成动作更连贯、更贴近真实运动模式
- 适合对动作生成、虚拟人建模感兴趣的科研与工程人员
人体动作生成旨在创建自然连贯的人体姿态序列，广泛应用于游戏、虚拟现实及人机交互领域。其目标是生成逼真的虚拟角色动作，增强虚拟代理表现力与沉浸式体验。以往研究多基于运动信号、音乐、文本或场景背景进行动作生成，但人体动作本身的复杂性及其与输入信号的关系常导致输出效果不理想。流形学习通过降低数据维度并捕捉有效动作子空间，提供了一种有效解决方案。本文系统综述了流形学习在人体动作生成中的应用，涵盖从非结构化数据中提取流形的方法、其在动作生成中的具体应用，并讨论了当前优势与未来方向。本综述是该领域较早的全面梳理，旨在为研究者提供宏观视角，激发对现有挑战的新应对思路。
原文摘要 · Abstract (English)
Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspaces of effective motion. In this review, we present a comprehensive overview of manifold applications in human motion generation, one of the first in this domain. We explore methods for extracting manifolds from unstructured data, their application in motion generation, and discuss their advantages and future directions. This survey aims to provide a broad perspective on the field and stimulate new approaches to ongoing challenges.
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