Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold
It addresses the challenge of producing realistic animations for virtual characters, which is crucial for enhancing immersive experiences in domains such as gaming and human-computer interaction, but is presented as a review rather than an incremental research contribution.
This review paper tackles the problem of generating lifelike human motion sequences for applications like gaming and virtual reality by exploring the use of manifold learning to reduce data complexity and capture effective motion subspaces, providing a comprehensive overview of methods and future directions in this emerging area.
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.