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

Jiayi Zhao, Dongdong Weng, Qiuxin Du, Zeyu Tian

TL;DR

This paper surveys the use of manifold learning to generate lifelike human motion, arguing that learning a low-dimensional motion manifold can capture the valid subspace of realistic motions and enable efficient, controllable generation. It covers motion representations (keypoint vs rotation-based) and formalizations of motion and phase manifolds, including practical formulations for phase features. The review surveys a progression of methods—from PCA/GPLVM foundations to deep learning approaches like convolutional autoencoders, STRNN, and periodic autoencoders—and discusses their roles in motion synthesis, real-time motion control, and in-betweening. The work highlights benefits of manifold-based approaches, such as reduced computation and smoother, more coherent motions, while identifying gaps in modeling environmental interactions and the need to encode external constraints into the manifold space for broader applicability. Overall, the paper advocates manifold-based strategies as a productive direction for advancing realistic, interactive digital characters in VR, gaming, and HCI contexts.

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.

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

TL;DR

This paper surveys the use of manifold learning to generate lifelike human motion, arguing that learning a low-dimensional motion manifold can capture the valid subspace of realistic motions and enable efficient, controllable generation. It covers motion representations (keypoint vs rotation-based) and formalizations of motion and phase manifolds, including practical formulations for phase features. The review surveys a progression of methods—from PCA/GPLVM foundations to deep learning approaches like convolutional autoencoders, STRNN, and periodic autoencoders—and discusses their roles in motion synthesis, real-time motion control, and in-betweening. The work highlights benefits of manifold-based approaches, such as reduced computation and smoother, more coherent motions, while identifying gaps in modeling environmental interactions and the need to encode external constraints into the manifold space for broader applicability. Overall, the paper advocates manifold-based strategies as a productive direction for advancing realistic, interactive digital characters in VR, gaming, and HCI contexts.

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.

Paper Structure

This paper contains 11 sections, 4 equations, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. PRELIMINARIES
  3. Motion Data Representation
  4. Manifolds
  5. LEARNING MOTION MANIFOLDS
  6. MANIFOLDS IN MOTION GENERATION
  7. Motion Synthesis
  8. Motion Controller
  9. Motion in-betweening
  10. CONCLUSION AND FUTURE WORK
  11. ACKNOWLEDGEMENT

Figures (2)

  • Figure 1: The typical concept of learning manifold from motion and motion generating with manifold has been proposed in the recent twenty years.
  • Figure 2: Typical human pose and shape representations in (a) keypoint-based and (b) rotation-based (c) Find a valid motion in the entire motion space. A point and B point represent two statuses, there are many different motions between A and B like green lines, only the motion on the surface is valid like the red line.