Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold

TL;DR

This paper reviews how manifold learning techniques are applied to generate realistic human motion sequences, addressing the complexity of motion data and improving virtual character animation.

Contribution

It provides one of the first comprehensive overviews of manifold learning applications in human motion generation, highlighting methods, advantages, and future research directions.

Findings

Manifold learning effectively reduces data dimensionality in motion generation.

Application of manifolds improves the realism of virtual human movements.

The survey identifies key challenges and promising future directions in the field.

Abstract

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.